Context-Aware Recommender Systems
Gediminas Adomavicius, Alexander Tuzhilin
Abstract The importance of contextual information has been recognized by re-
searchers and practitioners in many disciplines, including e-commerce personal-
ization, information retrieval, ubiquitous and mobile computing, data mining, mar-
keting, and management. While a substantial amount of research has already been
performed in the area of recommender systems, most existing approaches focus on
recommending the most relevant items to users without taking into account any ad-
ditional contextual information, such as time, location, or the company of other peo-
ple (e.g., for watching movies or dining out). In this chapter we argue that relevant
contextual information does matter in recommender systems and that it is important
to take this information into account when providing recommendations. We discuss
the general notion of context and how it can be modeled in recommender systems.
Furthermore, we introduce three different algorithmic paradigms – contextual pre-
filtering, post-filtering, and modeling – for incorporating contextual information into
the recommendation process, discuss the possibilities of combining several context-
aware recommendation techniques into a single unifying approach, and provide a
case study of one such combined approach. Finally, we present additional capabil-
ities for context-aware recommenders and discuss important and promising direc-
tions for future research.
Gediminas Adomavicius
Department of Information and Decision Sciences
Carlson School of Management, University of Minnesota
e-mail: gedas@umn.edu
Alexander Tuzhilin
Department of Information, Operations and Management Sciences
Stern School of Business, New York University
e-mail: atuzhili@stern.nyu.edu
1

2 Gediminas Adomavicius, Alexander Tuzhilin
1 Introduction and Motivation
The majority of existing approaches to recommender systems focus on recommend-
ing the most relevant items to individual users and do not take into consideration any
contextual information, such as time, place and the company of other people (e.g.,
for watching movies or dining out). In other words, traditionally recommender sys-
tems deal with applications having only two types of entities, users and items, and
do not put them into a context when providing recommendations.
However, in many applications, such as recommending a vacation package, per-
sonalized content on a Web site, or a movie, it may not be sufficient to consider only
users and items – it is also important to incorporate the contextual information into
the recommendation process in order to recommend items to users in certain cir-
cumstances. For example, using the temporal context, a travel recommender system
would provide a vacation recommendation in the winter that can be very different
from the one in the summer. Similarly, in the case of personalized content delivery
on a Web site, it is important to determine what content needs to be delivered (rec-
ommended) to a customer and when. More specifically, on weekdays a user might
prefer to read world news when she logs on in the morning and the stock market
report in the evening, and on weekends to read movie reviews and do shopping.
These observations are consistent with the findings in behavioral research on
consumer decision making in marketing that have established that decision making,
rather than being invariant, is contingent on the context of decision making. There-
fore, accurate prediction of consumer preferences undoubtedly depends upon the
degree to which the recommender system has incorporated the relevant contextual
information into a recommendation method.
More recently, companies started incorporating some contextual information into
their recommendation engines. For example, when selecting a song for the customer,
Sourcetone interactive radio (www.sourcetone.com) takes into the consideration the
current mood of the listener (the context) that she specified. In case of music recom-
menders, some of the contextual information, such as listener’s mood, may matter
for providing better recommendations. However, it is still not clear if context matters
for a broad range of other recommendation applications.
In this chapter we discuss the topic of context-aware recommender systems
(CARS), address this and several other related questions, and demonstrate that, de-
pending on the application domain and the available data, at least certain contextual
information can be useful for providing better recommendations. We also propose
three major approaches in which the contextual information can be incorporated
into recommender systems, individually examine these three approaches, and also
discuss how these three separate methods can be combined into one unified ap-
proach. Finally, the inclusion of the contextual information into the recommenda-
tion process presents opportunities for richer and more diverse interactions between
the end-users and recommender systems. Therefore, in this chapter we also discuss
novel flexible interaction capabilities in the form of the recommendation query lan-
guage for context-aware recommender systems.

Context-Aware Recommender Systems 3
The rest of the chapter is organized as follows. Section 2 discusses the general
notion of context as well as how it can be modeled in recommender systems. Sec-
tion 3 presents three different algorithmic paradigms for incorporating contextual
information into the recommendation process. Section 4 discusses the possibilities
of combining several context-aware recommendation techniques and provides a case
study of one such combined approach. Additional important capabilities for context-
aware recommender systems are described in Section 5, and the conclusions and
some opportunities for future work are presented in Section 6.
2 Context in Recommender Systems
Before discussing the role and opportunities of contextual information in recom-
mender systems, in Section 2.1 we start by discussing the general notion of context.
Then, in Section 2.2, we focus on recommender systems and explain how context is
specified and modeled there.
2.1 What is Context?
Context is a multifaceted concept that has been studied across different research dis-
ciplines, including computer science (primarily in artificial intelligence and ubiqui-
tous computing), cognitive science, linguistics, philosophy, psychology, and orga-
nizational sciences. In fact, an entire conference – CONTEXT (see, for example,
http://context-07.ruc.dk) – is dedicated exclusively to studying this topic and incor-
porating it into various other branches of science, including medicine, law, and busi-
ness. In reference to the latter, a well-known business researcher and practitioner C.
K. Prahalad has stated that “the ability to reach out and touch customers anywhere
at anytime means that companies must deliver not just competitive products but also
unique, real-time customer experiences shaped by customer context” and that this
would be the next main issue (“big thing”) for the CRM practitioners [56].
Since context has been studied in multiple disciplines, each discipline tends to
take its own idiosyncratic view that is somewhat different from other disciplines
and is more specific than the standard generic dictionary definition of context as
“conditions or circumstances which affect some thing” [69]. Therefore, there ex-
ist many definitions of context across various disciplines and even within specific
subfields of these disciplines. Bazire and Bre´zillon [16] present and examine 150
different definitions of context from different fields. This is not surprising, given the
complexity and the multifaceted nature of the concept. As Bazire and Bre´zillon [16]
observe:
“. . . it is difficult to find a relevant definition satisfying in any discipline. Is context a frame
for a given object? Is it the set of elements that have any influence on the object? Is it
possible to define context a priori or just state the effects a posteriori? Is it something static

4 Gediminas Adomavicius, Alexander Tuzhilin
or dynamic? Some approaches emerge now in Artificial Intelligence [. . .]. In Psychology, we
generally study a person doing a task in a given situation. Which context is relevant for our
study? The context of the person? The context of the task? The context of the interaction?
The context of the situation? When does a context begin and where does it stop? What are
the real relationships between context and cognition?”
Since we focus on recommender systems in this paper and since the general
concept of context is very broad, we try to focus on those fields that are directly
related to recommender systems, such as data mining, e-commerce personalization,
databases, information retrieval, ubiquitous and mobile context-aware systems, mar-
keting, and management. We follow Palmisano et al. [53] in this section when de-
scribing these areas.
DataMining. In the data mining community, context is sometimes defined as those
events which characterize the life stages of a customer and that can determine a
change in his/her preferences, status, and value for a company [17]. Examples of
context include a new job, the birth of a child, marriage, divorce, and retirement.
Knowledge of this contextual information helps (a) mining patterns pertaining to
this particular context by focusing only on the relevant data; for example, the data
pertaining to the daughter’s wedding, or (b) selecting only relevant results, i.e., those
data mining results that are applicable to the particular context, such as the discov-
ered patterns that are related to the retirement of a person.
E-commerce Personalization. Palmisano et al. [53] use the intent of a purchase
made by a customer in an e-commerce application as contextual information. Dif-
ferent purchasing intents may lead to different types of behavior. For example, the
same customer may buy from the same online account different products for dif-
ferent reasons: a book for improving her personal work skills, a book as a gift, or
an electronic device for her hobby. To deal with different purchasing intentions,
Palmisano et al. [53] build a separate profile of a customer for each purchasing con-
text, and these separate profiles are used for building separate models predicting
customer’s behavior in specific contexts and for specific segments of customers.
Such contextual segmentation of customers is useful, because it results in better
predictive models across different e-commerce applications [53].
Recommender systems are also related to e-commerce personalization, since per-
sonalized recommendations of various products and services are provided to the
customers. The importance of including and using the contextual information in rec-
ommendation systems has been demonstrated in [3], where the authors presented a
multidimensional approach that can provide recommendations based on contextual
information in addition to the typical information on users and items used in many
recommendation applications. It was also demonstrated by Adomavicius et al. [3]
that the contextual information does matter in recommender systems: it helps to
increase the quality of recommendations in certain settings.
Similarly, Oku et al. [52] incorporate additional contextual dimensions (such as
time, companion, and weather) into the recommendation process and use machine
learning techniques to provide recommendations in a restaurant recommender sys-
tem. They empirically show that the context-aware approach significantly outper-

Context-Aware Recommender Systems 5
forms the corresponding non-contextual approach in terms of recommendation ac-
curacy and user’s satisfaction with recommendations.
Since we focus on the use of context in recommender systems in this paper, we
will describe these and similar approaches later in the chapter.
Ubiquitous and mobile context-aware systems. In the literature pertaining to the
context-aware systems, context was initially defined as the location of the user, the
identity of people near the user, the objects around, and the changes in these ele-
ments [62]. Other factors have been added to this definition subsequently. For in-
stance, Brown et al. [22] include the date, the season, and the temperature. Ryan
et al. [60] add the physical and conceptual statuses of interest for a user. Dey et al.
[32] include the user’s emotional status and broaden the definition to any informa-
tion which can characterize and is relevant to the interaction between a user and an
application. Some associate the context with the user [32, 34], while others empha-
size how context relates to the application [59, 68]. More recently, a number of other
techniques for context-aware systems have been discussed in research literature, in-
cluding hybrid techniques for mobile applications [58, 70] and graphical models for
visual recommendation [19].
This contextual information is crucial for providing a broad range of Location-
Based Services (LBSes) to the mobile customers [63]. For example, a Broadway the-
ater may want to recommend heavily discounted theater tickets to the Time Square
visitors in New York thirty minutes before the show starts (since these tickets will
be wasted anyway after the show begins) and send this information to the visitors’
smart phones or other communication devices. Note that time, location, and the type
of the communication device (e.g., smart phone) constitute contextual information
in this application. Brown et al. [21] introduce another interesting application that
allows tourists interactively share their sightseeing experiences with remote users,
demonstrating the value that context-aware techniques can provide in supporting
social activities.
A survey of context-aware mobile computing research can be found in [29],
which discusses different models of contextual information, context-sensing tech-
nologies, different possible architectures, and a number of context-aware application
examples.
Databases. Contextual capabilities have been added to some of the database man-
agement systems by incorporating user preferences and returning different answers
to database queries depending on the context in which the queries have been ex-
pressed and the particular user preferences corresponding to specific contexts. More
specifically, in Stephanidis et al. [65] a set of contextual parameters is introduced
and preferences are defined for each combination of regular relational attributes and
these contextual parameters. Then Stephanidis et al. [65] present a context-aware
extension of SQL to accommodate for such preferences and contextual informa-
tion. Agrawal et al. [7] present another method for incorporating context and user
preferences into query languages and develop methods of reconciling and ranking
different preferences in order to expeditiously provide ranked answers to contextual
queries. Mokbel and Levandoski [51] describe the context-aware and location-aware

6 Gediminas Adomavicius, Alexander Tuzhilin
database server CoreDB and discuss several issues related to its implementation,
including challenges related to context-aware query operators, continuous queries,
multi-objective query processing, and query optimization.
Information Retrieval. Contextual information has been proven to be helpful in
information retrieval and access [39], although most existing systems base their re-
trieval decisions solely on queries and document collections, whereas information
about search context is often ignored [9]. The effectiveness of a proactive retrieval
system depends on the ability to perform context-based retrieval, generating queries
which return context-relevant results [45, 64]. In Web searching, context is consid-
ered as the set of topics potentially related to the search term. For instance, Lawrence
[44] describes how contextual information can be used and proposes several special-
ized domain-specific context-based search engines. Integration of context into the
Web services composition is suggested by Maamar et al. [50]. Most of the current
context-aware information access and retrieval techniques focus on the short-term
problems and immediate user interests and requests (such as “find all files created
during a spring meeting on a sunny day outside an Italian restaurant in New York”),
and are not designed to model long-term user tastes and preferences.
Marketing and Management. Marketing researchers have maintained that the
purchasing process is contingent upon the context in which the transaction takes
place, since the same customer can adopt different decision strategies and prefer
different products or brands depending on the context [18, 49]. According to Lilien
et al. [46], “consumers vary in their decision-making rules because of the usage sit-
uation, the use of the good or service (for family, for gift, for self) and purchase
situation (catalog sale, in-store shelf selection, and sales person aided purchase).”
Therefore, accurate predictions of consumer preferences should depend on the de-
gree to which we have incorporated the relevant contextual information. In the mar-
keting literature, context has been also studied in the field of behavioral decision
theory. In Lussier and Olshavsky [49], context is defined as a task complexity in the
brand choice strategy.
The context is defined in Prahalad [56] as “the precise physical location of a cus-
tomer at any given time, the exact minute he or she needs the service, and the kind
of technological mobile device over which that experience will be received.” Fur-
ther, Prahalad [56] focuses on the applications where the contextual information is
used for delivering “unique, real-time customer experiences” based on this contex-
tual information, as opposed to the delivery of competitive products. Prahalad [56]
provides an example about the case when he left his laptop in a hotel in Boston,
and was willing to pay significant premiums for the hotel shipping the laptop to him
in New York in that particular context (he was in New York and needed the laptop
really urgently in that particular situation).
To generalize his statements, Prahalad [56] really distinguishes among the fol-
lowing three dimensions of the contextual information: temporal (when to deliver
customer experiences), spatial (where to deliver), and technological (how to de-
liver). Although Prahalad focuses on the real-time experiences (implying that it is
really the present time, “now”), the temporal dimension can be generalized to the

Context-Aware Recommender Systems 7
past and the future (e.g., I want to see a movie tomorrow in the evening).
As this section clearly demonstrates, context is a multifaceted concept used
across various disciplines, each discipline taking a certain angle and putting its
“stamp” on this concept. To bring some “order” to this diversity of views, Dourish
[33] introduces taxonomy of contexts, according to which contexts can be classified
into the representational and the interactional views. In the representational view,
context is defined with a predefined set of observable attributes, the structure (or
schema, using database terminology) of which does not change significantly over
time. In other words, the representational view assumes that the contextual attributes
are identifiable and known a priori and, hence, can be captured and used within the
context-aware applications. In contrast, the interactional view assumes that the user
behavior is induced by an underlying context, but that the context itself is not neces-
sarily observable. Furthermore, Dourish [33] assumes that different types of actions
may give rise to and call for different types of relevant contexts, thus assuming a
bidirectional relationship between activities and underlying contexts: contexts in-
fluence activities and also different activities giving rise to different contexts.
In the next section, we take all these different definitions and approaches to con-
text and adapt them to the idiosyncratic needs of recommender systems. As a result,
we will also revise and enhance the prior definitions of context used in recommender
systems, including those provided in [3, 52, 71].
2.2 Modeling Contextual Information in Recommender Systems
Recommender systems emerged as an independent research area in the mid-1990s,
when researchers and practitioners started focusing on recommendation problems
that explicitly rely on the notion of ratings as a way to capture user preferences
for different items. For example, in case of a movie recommender system, John
Doe may assign a rating of 7 (out of 10) for the movie “Gladiator,” i.e., set
Rmovie(John Doe, Gladiator)=7. The recommendation process typically starts with
the specification of the initial set of ratings that is either explicitly provided by the
users or is implicitly inferred by the system. Once these initial ratings are specified,
a recommender system tries to estimate the rating function R
R : User× Item→ Rating
for the (user, item) pairs that have not been rated yet by the users. Here Rating is
a totally ordered set (e.g., non-negative integers or real numbers within a certain
range), and User and Item are the domains of users and items respectively. Once the
function R is estimated for the whole User × Item space, a recommender system
can recommend the highest-rated item (or k highest-rated items) for each user. We
call such systems traditional or two-dimensional (2D) since they consider only the
User and Item dimensions in the recommendation process.

8 Gediminas Adomavicius, Alexander Tuzhilin
In other words, in its most common formulation, the recommendation problem
is reduced to the problem of estimating ratings for the items that have not been seen
by a user. This estimation is usually based on the ratings given by this user to other
items, ratings given to this item by other users, and possibly on some other infor-
mation as well (e.g., user demographics, item characteristics). Note that, while a
substantial amount of research has been performed in the area of recommender sys-
tems, the vast majority of the existing approaches focus on recommending items to
users or users to items and do not take into the consideration any additional contex-
tual information, such as time, place, the company of other people (e.g., for watching
movies). Motivated by this, in this chapter we explore the area of context-aware rec-
ommender systems (CARS), which deal with modeling and predicting user tastes and
preferences by incorporating available contextual information into the recommen-
dation process as explicit additional categories of data. These long-term preferences
and tastes are usually expressed as ratings and are modeled as the function of not
only items and users, but also of the context. In other words, ratings are defined with
the rating function as
R : User× Item×Context → Rating,
where User and Item are the domains of users and items respectively, Rating is the
domain of ratings, and Context specifies the contextual information associated with
the application. To illustrate these concepts, consider the following example.
Example 1. Consider the application for recommending movies to users,
where users and movies are described as relations having the following at-
tributes:
• Movie: the set of all the movies that can be recommended; it is defined as
Movie(MovieID, Title, Length, ReleaseYear, Director, Genre).
• User: the people to whom movies are recommended; it is defined as
User(UserID, Name, Address, Age, Gender, Profession).
Further, the contextual information consists of the following three types that
are also defined as relations having the following attributes:
• Theater: the movie theaters showing the movies; it is defined as The-
ater(TheaterID, Name, Address, Capacity, City, State, Country).
• Time: the time when the movie can be or has been seen; it is defined
as Time(Date, DayOfWeek, TimeOfWeek, Month, Quarter, Year). Here,
attribute DayOfWeek has values Mon, Tue, Wed, Thu, Fri, Sat, Sun, and
attribute TimeOfWeek has values “Weekday” and “Weekend”.
• Companion: represents a person or a group of persons with whom one can
see a movie. It is defined as Companion(companionType), where attribute
companionType has values “alone”, “friends”, “girlfriend/boyfriend”, “fam-
ily”, “co-workers”, and “others”.
Then the rating assigned to a movie by a person also depends on where and
how the movie has been seen, with whom, and at what time. For example,

Context-Aware Recommender Systems 9
the type of movie to recommend to college student Jane Doe can differ sig-
nificantly depending on whether she is planning to see it on a Saturday night
with her boyfriend vs. on a weekday with her parents.
As we can see from this example and other cases, the contextual information
Context can be of different types, each type defining a certain aspect of context,
such as time, location (e.g., Theater), companion (e.g., for seeing a movie), purpose
of a purchase, etc. Further, each contextual type can have a complicated structure
reflecting complex nature of the contextual information. Although this complex-
ity of contextual information can take many different forms, one popular defining
characteristic is the hierarchical structure of contextual information that can be rep-
resented as trees, as is done in most of the context-aware recommender and profiling
systems, including [3] and [53]. For instance, the three contexts from Example 1 can
have the following hierarchies associated with them: Theater: TheaterID → City →
State → Country; Time: Date → DayOfWeek → TimeOfWeek, Date → Month →
Quarter → Year.1
Furthermore, we follow the representational view of Dourish [33], as described
at the end of Section 2.1, and assume that the context is defined with a predefined
set of observable attributes, the structure of which does not change significantly
over time. Although there are some papers in the literature that take the interac-
tional approach to modeling contextual recommendations, such as [11] that models
context through a short-term memory (STM) interactional approach borrowed from
psychology, most of the work on context-aware recommender systems follows the
representational view. As stated before, we also adopt this representational view in
this chapter and assume that there is a predefined finite set of contextual types in a
given application and that each of these types has a well-defined structure.
More specifically, we follow Palmisano et al. [53], and also Adomavicius et al.
[3] to some extent, in this paper and define the contextual information with a set
of contextual dimensions K, each contextual dimension K in K being defined by a
set of q attributes K = (K1, . . . ,Kq) having a hierarchical structure and capturing a
particular type of context, such as Time or CommunicatingDevice. The values taken
by attribute Kq define finer (more granular) levels, while K1 values define coarser
(less granular) levels of contextual knowledge. For example, Figure 1(a) presents
a four-level hierarchy for the contextual attribute K specifying the intent of a pur-
chasing transaction in an e-retailer application. While the root (coarsest level) of the
hierarchy for K defines purchases in all possible contexts, the next level is defined
by attribute K1 = {Personal, Gift}, which labels each customer purchase either as a
personal purchase or as a gift. At the next, finer level of the hierarchy, “Personal”
value of attribute K1 is further split into a more detailed personal context: personal
purchase made for the work-related or other purposes. Similarly, the Gift value for
K1 can be split into a gift for a partner or a friend and a gift for parents or others.
1 For the sake of completeness, we would like to point out that not only the contextual dimen-
sions, but also the traditional User and Item dimensions can have their attributes form hierarchical
relationships. For example, the main two dimensions from Example 1 can have the following hier-
archies associated with them:Movie: MovieID→Genre;User: UserID→Age, UserID→Gender,
UserID → Profession.

10 Gediminas Adomavicius, Alexander Tuzhilin
Thus, the K2 level is K2 = {PersonalWork, PersonalOther, GiftPartner/Friend, Gift-
Parent/Other}. Finally, attribute K2 can be split into further levels of hierarchy, as
shown in Figure 1(a).2
Fig. 1 Contextual information hierarchical structure: (a) e-retailer dataset, (b) food dataset [53].
Contextual information was also defined in [3] as follows. In addition to the clas-
sical User and Item dimensions, additional contextual dimensions, such as Time,
Location, etc., were also introduced using the OLAP-based3 multidimensional
data (MD) model widely used in the data warehousing applications in databases
[28, 40]. Formally, let D1,D2, . . . ,Dn be dimensions, two of these dimensions be-
ing User and Item, and the rest being contextual. Each dimension Di is a sub-
set of a Cartesian product of some attributes (or fields) Ai j,( j = 1, . . . ,ki), i.e.,
Di ⊆ Ai1 ×Ai2 × . . .×Aiki , where each attribute defines a domain (or a set) of val-
ues. Moreover, one or several attributes form a key, i.e., they uniquely define the
rest of the attributes [57]. In some cases, a dimension can be defined by a single
attribute, and ki =1 in such cases. For example, consider the three-dimensional rec-
ommendation space User× Item×Time, where the User dimension is defined as
User ⊆ UName×Address× Income×Age and consists of a set of users having
certain names, addresses, incomes, and being of a certain age. Similarly, the Item
dimension is defined as Item⊆ IName×Type×Price and consists of a set of items
defined by their names, types and the price. Finally, the Time dimension can be de-
fined as Time⊆Year×Month×Day and consists of a list of days from the starting
to the ending date (e.g. from January 1, 2003 to December 31, 2003).
Given dimensions D1,D2, . . . ,Dn, we define the recommendation space for these
dimensions as a Cartesian product S = D1 ×D2 × . . .Dn. Moreover, let Rating be
a rating domain representing the ordered set of all possible rating values. Then the
rating function is defined over the space D1× . . .×Dn as
R : D1× . . .×Dn → Rating.
2 For simplicity and illustration purposes, this figure uses only two-way splits. Obviously, three-
way, four-way and, more generally, multi-way splits are also allowed.
3 OLAP stands for OnLine Analytical Processing, which represents a popular approach to manip-
ulation and analysis of data stored in multi-dimensional cube structures and which is widely used
for decision support.

Context-Aware Recommender Systems 11
For instance, continuing the User × Item × Time example considered above, we
can define a rating function R on the recommendation space User × Item × Time
specifying how much user u ∈ User liked item i ∈ Item at time t ∈ Time, R(u, i, t).
Visually, ratings R(d1, . . . ,dn) on the recommendation space S = D1×D2× . . .×
Dn can be stored in a multidimensional cube, such as the one shown in Figure 2. For
example, the cube in Figure 2 stores ratings R(u, i, t) for the recommendation space
User × Item× Time, where the three tables define the sets of users, items, and times
associated with User, Item, and Time dimensions respectively. For example, rating
R(101,7,1) = 6 in Figure 2 means that for the user with User ID 101 and the item
with Item ID 7, rating 6 was specified during the weekday.
Fig. 2 Multidimensional model for the User × Item × Time recommendation space.
The rating function R introduced above is usually defined as a partial function,
where the initial set of ratings is known. Then, as usual in recommender systems,
the goal is to estimate the unknown ratings, i.e., make the rating function R total.
The main difference between the multidimensional (MD) contextual model de-
scribed above and the previously described contextual model lies in that contextual
information in the MD model is defined using classical OLAP hierarchies, whereas
the contextual information in the previous case is defined with more general hierar-
chical taxonomies, that can be represented as trees (both balanced and unbalanced),
directed acyclic graphs (DAGs), or various other types of taxonomies. Further, the

12 Gediminas Adomavicius, Alexander Tuzhilin
ratings in the MD model are stored in the multidimensional cubes, whereas the rat-
ings in the other contextual model are stored in more general hierarchical structures.
We would also like to point out that not all contextual information might be
relevant or useful for recommendation purposes. Consider, for example, a book rec-
ommender system. Many types of contextual data could potentially be obtained by
such a system from book buyers, including: (a) purpose of buying the book (possi-
ble options: for work, for leisure, . . .); (b) planned reading time (weekday, weekend,
. . .); (c) planned reading place (at home, at school, on a plane, . . .); (d) the value of
the stock market index at the time of the purchase. Clearly some types of contextual
information can be more relevant in a given application than some other types. For
example, in the previous example, the value of a stock market can be less relevant
as contextual information than the purpose of buying a book. There are several ap-
proaches to determining the relevance of a given type of contextual information.
In particular, the relevance determination can either be done manually, e.g., using
domain knowledge of the recommender system’s designer or a market expert in a
given application domain, or automatically, e.g., using numerous existing feature
selection procedures from machine learning [41], data mining [47], and statistics
[27], based on existing ratings data during the data preprocessing phase. The de-
tailed discussion of the specific feature selection procedures is beyond the scope of
this discussion; in the remainder of this chapter we will assume that only the relevant
contextual information is stored in the data.
2.3 Obtaining Contextual Information
The contextual information can be obtained in a number of ways, including:
• Explicitly, i.e., by directly approaching relevant people and other sources of con-
textual information and explicitly gathering this information either by asking di-
rect questions or eliciting this information through other means. For example, a
website may obtain contextual information by asking a person to fill out a web
form or to answer some specific questions before providing access to certain web
pages.
• Implicitly from the data or the environment, such as a change in location of the
user detected by a mobile telephone company. Alternatively, temporal contex-
tual information can be implicitly obtained from the timestamp of a transaction.
Nothing needs to be done in these cases in terms of interacting with the user or
other sources of contextual information – the source of the implicit contextual
information is accessed directly and the data is extracted from it.
• Inferring the context using statistical or data mining methods. For example, the
household identity of a person flipping the TV channels (husband, wife, son,
daughter, etc.) may not be explicitly known to a cable TV company; but it can
be inferred with reasonable accuracy by observing the TV programs watched and
the channels visited using various data mining methods. In order to infer this con-
textual information, it is necessary to build a predictive model (i.e., a classifier)

Context-Aware Recommender Systems 13
and train it on the appropriate data. The success of inferring this contextual in-
formation depends very significantly on the quality of such classifier, and it also
varies considerably across different applications. For example, it was demon-
strated in [53] that various types of contextual information can be inferred with a
reasonably high degree of accuracy in certain applications and using certain data
mining methods, such as Naı¨ve Bayes classifiers and Bayesian Networks.
Finally, the contextual information can be “hidden” in the data in some latent
form, and we can use it implicitly to better estimate the unknown ratings without ex-
plicitly knowing this contextual information. For instance, in the previous example,
we may want to estimate how much a person likes a particular TV program by mod-
eling the member of the household (husband, wife, etc.) watching the TV program
as a latent variable. It was also shown in [53] that this deployment of latent variables,
such as intent of purchasing a product (e.g., for yourself vs. as a gift, work-related vs.
pleasure, etc.), whose true values were unknown but that were explicitly modeled as
a part of a Bayesian Network (BN), indeed improved the predictive performance of
that BN classifier. Therefore, even without any explicit knowledge of the contextual
information (e.g., which member of the household is watching the program), recom-
mendation accuracy can still be improved by modeling and inferring this contextual
information implicitly using carefully chosen learning techniques (e.g., by using la-
tent variables inside well-designed recommendation models). A similar approach of
using latent variables is presented in [11].
As explained in Section 2.1, we focus on the representational view of Dourish
[33], and assume that the context is defined with a predefined set of contextual at-
tributes, the structure of which does not change over time. The implication of this
assumption is that we need to identify and acquire contextual information before
actual recommendations are made. If the acquisition process of this contextual in-
formation is done explicitly or even implicitly, it should be conducted as a part of
the overall data collection process. All this implies that the decisions of which con-
textual information should be relevant and collected for an application should be
done at the application design stage and well in advance of the time when actual
recommendations are provided.
One methodology of deciding which contextual attributes should be used in a
recommendation application (and which should not) is presented in [3]. In particu-
lar, Adomavicius et al. [3] propose that a wide range of contextual attributes should
be initially selected by the domain experts as possible candidates for the contextual
attributes for the application. For example, in a movie recommendation application
described in Example 1, we can initially consider such contextual attributes as Time,
Theater, Companion, Weather, as well as a broad set of other contextual attributes
that can possibly affect the movie watching experiences, as initially identified by
the domain experts for the application. Then, after collecting the data, including the
rating data and the contextual information, we may apply various types of statisti-
cal tests identifying which of the chosen contextual attributes are truly significant
in the sense that they indeed affect movie watching experiences, as manifested by
significant deviations in ratings across different values of a contextual attribute. For
example, we may apply pairwise t-tests to see if good weather vs. bad weather or

14 Gediminas Adomavicius, Alexander Tuzhilin
seeing a movie alone vs. with a companion significantly affect the movie watching
experiences (as indicated by statistically significant changes in rating distributions).
This procedure provides an example of screening all the initially considered con-
textual attributes and filtering out those that do not matter for a particular recom-
mendation application. For example, we may conclude that the Time, Theater and
Companion contexts matter, while the Weather context does not in the considered
movie recommendation application.
3 Paradigms for Incorporating Context in Recommender
Systems
The usage of contextual information in recommender systems can be traced to the
work by Herlocker and Konstan [35], who hypothesized that the inclusion of knowl-
edge about the user’s task into the recommendation algorithm in certain applications
can lead to better recommendations. For example, if we want to recommend books
as gifts for a child, then we might want to specify several books that the child al-
ready has (and likes) and provide this information (i.e., a task profile) to the rec-
ommender system for calculating new recommendations. Note that this approach
operates within the traditional 2D User × Item space, since the task specification
for a specific user consists of a list of sample items; in other words, besides the
standard User and Item dimensions, no additional contextual dimensions are used.
However, this approach serves as a successful illustration of how additional rele-
vant information (in the form of user-specified task-relevant item examples) can be
incorporated into the standard collaborative filtering paradigm. Further, the use of
interest scores assigned to topics has been applied to building contextual user pro-
files in recommender systems [72].
Different approaches to using contextual information in the recommendation pro-
cess can be broadly categorized into two groups: (1) recommendation via context-
driven querying and search, and (2) recommendation via contextual preference elic-
itation and estimation. The context-driven querying and search approach has been
used by a wide variety of mobile and tourist recommender systems [2, 26, 67].
Systems using this approach typically use contextual information (obtained either
directly from the user, e.g., by specifying current mood or interest, or from the envi-
ronment, e.g., obtaining local time, weather, or current location) to query or search
a certain repository of resources (e.g., restaurants) and present the best matching
resources (e.g., nearby restaurants that are currently open) to the user. One of the
early examples of this approach is the Cyberguide project [2], which developed
several tour guide prototypes for different hand-held platforms. Abowd et al. [2]
discuss different architectures and features necessary to provide realistic tour guide
services to mobile users and, more specifically, the role that the contextual knowl-
edge of the user’s current and past locations can play in the recommendation and
guiding process. Among the many other examples of context-aware tourist guide

Context-Aware Recommender Systems 15
systems proposed in research literature we can mention GUIDE [30], INTRIGUE
[13], COMPASS [67], and MyMap [31] systems.
The other general approach to using contextual information in the recommenda-
tion process, i.e., via contextual preference elicitation and estimation, represents a
more recent trend in context-aware recommender systems literature [3, 52, 54, 71].
In contrast to the previously discussed context-driven querying and search approach
(where the recommender systems use the current context information and speci-
fied current user’s interest as queries to search for the most appropriate content),
techniques that follow this second approach attempt to model and learn user pref-
erences, e.g., by observing the interactions of this and other users with the systems
or by obtaining preference feedback from the user on various previously recom-
mended items. To model users’ context-sensitive preferences and generate recom-
mendations, these techniques typically either adopt existing collaborative filtering,
content-based, or hybrid recommendation methods to context-aware recommenda-
tion settings or apply various intelligent data analysis techniques from data mining
or machine learning (such as Bayesian classifiers or support vector machines).
While both general approaches offer a number of research challenges, in the
remainder of this chapter we will focus on the second, more recent trend of the con-
textual preference elicitation and estimation in recommender systems. We do want
to mention that it is possible to design applications that combine the techniques
from both general approaches (i.e., both context-driven querying and search as well
as contextual preference elicitation and estimation) into a single system. For exam-
ple, the UbiquiTO system [26], which implements a mobile tourist guide, provides
intelligent adaptation not only based on the specific context information, but also
uses various rule-based and fuzzy set techniques to adapt the application content
based on the user preferences and interests. Similarly, the News@hand system [25]
uses semantic technologies to provide personalized news recommendations that are
retrieved using user’s concept-based queries or calculated according to a specific
user’s (or a user group’s) profile.
To start the discussion of the contextual preference elicitation and estimation
techniques, note that, in its general form, a traditional 2-dimensional (2D) (User×
Item) recommender system can be described as a function, which takes partial user
preference data as its input and produces a list of recommendations for each user
as an output. Accordingly, Figure 3 presents a general overview of the traditional
2D recommendation process, which includes three components: data (input), 2D
recommender system (function), and recommendation list (output). Note that, as
indicated in Figure 3, after the recommendation function is defined (or constructed)
based on the available data, recommendation list for any given user u is typically
generated by using the recommendation function on user u and all candidate items
to obtain a predicted rating for each of the items and then by ranking all items
according to their predicted rating value. Later in this section, we will discuss how
the use of contextual information in each of those three components gives rise to
three different paradigms for context-aware recommender systems.

16 Gediminas Adomavicius, Alexander Tuzhilin
Fig. 3 General components of the traditional recommendation process.
As mentioned in Section 2.2, traditional recommender systems are built based
on the knowledge of partial user preferences, i.e., user preferences for some (of-
ten limited) set of items, and the input data for traditional recommender systems
is typically based on the records of the form < user, item,rating >. In contrast,
context-aware recommender systems are built based on the knowledge of par-
tial contextual user preferences and typically deal with data records of the form
< user, item,context,rating >, where each specific record includes not only how
much a given user liked a specific item, but also the contextual information in which
the item was consumed by this user (e.g., Context = Saturday). Also, in addition
to the descriptive information about users (e.g., demographics), items (e.g., item
features), and ratings (e.g., multi-criteria rating information), context-aware recom-
mender systems may also make use of additional context attributes, such as context
hierarchies (e.g., Saturday → Weekend) mentioned in Section 2.2. Based on the
presence of this additional contextual data, several important questions arise: How
contextual information should be reflected when modeling user preferences? Can
we reuse the wealth of knowledge in traditional (non-contextual) recommender sys-
tems to generate context-aware recommendations? We will explore these questions
in this chapter in more detail.
In the presence of available contextual information, following the diagrams in
Figure 4, we start with the data having the form U × I×C×R, where C is addi-
tional contextual dimension and end up with a list of contextual recommendations
i1, i2, i3 . . . for each user. However, unlike the process in Figure 3, which does not
take into account the contextual information, we can apply the information about the
current (or desired) context c at various stages of the recommendation process. More
specifically, the context-aware recommendation process that is based on contextual
user preference elicitation and estimation can take one of the three forms, based on
which of the three components the context is used in, as shown in Figure 4:
• Contextual pre-filtering (or contextualization of recommendation input). In this
recommendation paradigm (presented in Figure 4a), contextual information drives
data selection or data construction for that specific context. In other words, in-
formation about the current context c is used for selecting or constructing the
relevant set of data records (i.e., ratings). Then, ratings can be predicted using
any traditional 2D recommender system on the selected data.
• Contextual post-filtering (or contextualization of recommendation output). In this
recommendation paradigm (presented in Figure 4b), contextual information is
initially ignored, and the ratings are predicted using any traditional 2D recom-

Context-Aware Recommender Systems 17
mender system on the entire data. Then, the resulting set of recommendations is
adjusted (contextualized) for each user using the contextual information.
• Contextual modeling (or contextualization of recommendation function). In this
recommendation paradigm (presented in Figure 4c), contextual information is
used directly in the modeling technique as part of rating estimation.
Fig. 4 Paradigms for incorporating context in recommender systems.
In the remainder of this section we will discuss these three approaches in detail.
3.1 Contextual Pre-Filtering
As shown in Figure 4a, the contextual pre-filtering approach uses contextual infor-
mation to select or construct the most relevant 2D (User × Item) data for generating
recommendations. One major advantage of this approach is that it allows deploy-
ment of any of the numerous traditional recommendation techniques previously
proposed in the literature [5]. In particular, in one possible use of this approach,
context c essentially serves as a query for selecting (filtering) relevant ratings data.
An example of a contextual data filter for a movie recommender system would be:
if a person wants to see a movie on Saturday, only the Saturday rating data is used
to recommend movies. Note that this example represents an exact pre-filter. In other
words, the data filtering query has been constructed using exactly the specified con-
text.
For example, following the contextual pre-filtering paradigm, Adomavicius et al.
[3] proposed a reduction-based approach, which reduces the problem of multidi-

18 Gediminas Adomavicius, Alexander Tuzhilin
mensional (MD) contextual recommendations to the standard 2D User × Item rec-
ommendation space. Therefore, as with any contextual pre-filtering approach, one
important benefit of the reduction-based approach is that all the previous research on
2D recommender systems is directly applicable in the MD case after the reduction
is done. In particular, let RDUser×Item: U × I → Rating be any 2D rating estimation
function that, given existing ratingsD (i.e.,D contains records< user, item,rating>
for each of the known, user-specified ratings), can calculate a prediction for any rat-
ing, e.g., RDUser×Item(John,StarWars). Then, a 3-dimensional rating prediction func-
tion supporting the context of time can be defined similarly as RDUser×Item×Time :
U × I×T → Rating, where D contains records < user, item, time,rating > for the
user-specified ratings. Then the 3-dimensional prediction function can be expressed
through a 2D prediction function in several ways, including:
∀(u, i, t) ∈U × I×T,RDUser×Item×Time(u, i, t) = R
D[Time=t](User,Item,Rating)
User×Item (u, i).
Here [Time = t] denotes a simple contextual pre-filter, and D[Time = t](User,
Item, Rating) denotes a rating dataset obtained from D by selecting only the records
where Time dimension has value t and keeping only the values for User and Item
dimensions, as well as the value of the rating itself. I.e., if we treat a dataset of 3-
dimensional ratings D as a relation, then D[Time = t](User, Item,Rating) is simply
another relation obtained from D by performing two relational operations: selection
and, subsequently, projection.
However, the exact context sometimes can be too narrow. Consider, for example,
the context of watching a movie with a girlfriend in a movie theater on Saturday
or, i.e., c = (Girlfriend, Theater, Saturday). Using this exact context as a data fil-
tering query may be problematic for several reasons. First, certain aspects of the
overly specific context may not be significant. For example, user’s movie watching
preferences with a girlfriend in a theater on Saturday may be exactly the same as
on Sunday, but different from Wednesday’s. Therefore, it may be more appropri-
ate to use a more general context specification, i.e., Weekend instead of Saturday.
And second, exact context may not have enough data for accurate rating prediction,
which is known as the “sparsity” problem in recommender systems literature. In
other words, the recommender system may not have enough data points about the
past movie watching preferences of a given user with a girlfriend in a theater on
Saturday.
Context generalization. Adomavicius et al. [3] introduce the notion of general-
ized pre-filtering, which allows to generalize the data filtering query obtained based
on a specified context. More formally, let’s define c′ = (c′1, . . . ,c′k) to be a general-
ization of context c = (c1, . . . ,ck) if and only if ci → c′i for every i = 1, . . . ,k in the
corresponding context hierarchy. Then, c′ (instead of c) can be used as a data query
to obtain contextualized ratings data.
Following the idea of context generalization, Adomavicius et al. [3] proposed to
use not a simple pre-filter [Time = t], which represents the exact context t of the
rating (u, i, t), but rather a generalized pre-filter [Time ∈ St], where St denotes some

Context-Aware Recommender Systems 19
superset of context t. Here St is called a contextual segment [3]. For example, if we
would like to predict howmuch John Doe would like to see the “Gladiator” movie on
Monday, i.e., to calculate RDUser×Item×Time(JohnDoe,Gladiator,Monday), we could
use not only other user-specified Monday ratings for prediction, but Weekday ratings
in general. In other words, for every (u, i, t) where t ∈Weekday, we can predict the
rating as RDUser×Item×Time(u, i, t) = R
D[Time∈Weekday](User,Item,AGGR(Rating))
User×Item (u, i). More
generally, in order to estimate some rating R(u, i, t), we can use some specific con-
textual segment St as: RDUser×Item×Time(u, i, t)=R
D[Time∈St ](User,Item,AGGR(Rating))
User×Item (u, i).
Note, that we have used the AGGR(Rating) notation in the above expressions,
since there may be several user-specified ratings with the same User and Item values
for different Time instances in dataset D belonging to some contextual segment St
(e.g., different ratings for Monday and Tuesday, all belonging to segment Weekday).
Therefore, we have to aggregate these values using some aggregation function, e.g.,
averaging, when reducing the dimensionality of the recommendation space. The
above 3-dimensional reduction-based approach can be extended to a general pre-
filtering method reducing an arbitrary n-dimensional recommendation space to an
m-dimensional one (wherem< n). In this chapter we will assume thatm = 2 because
traditional recommendation algorithms are only designed for the two-dimensional
User× Item case. Note, that there typically exist multiple different possibilities for
context generalization, based on the context taxonomy and the desired context gran-
ularity. For example, let’s assume that we have the following contextual taxonomies
(is-a or belongs-to relationships) that can be derived from context hierarchies:
• Company: Girlfriend → Friends → NotAlone → AnyCompany;
• Place: Theater → AnyPlace;
• Time: Saturday → Weekend → AnyTime.
Then, the following are just several examples of possible generalizations c′ of the
above-mentioned context c = (Girlfriend, Theater, Saturday):
• c′ = (Girlfriend, AnyPlace, Saturday);
• c′ = (Friends, Theater, AnyTime);
• c′ = (NotAlone, Theater, Weekend);
Therefore, choosing the “right” generalized pre-filter becomes an important
problem. One option is to use a manual, expert-driven approach; e.g., always gener-
alize specific days of week into more general Weekday or Weekend. Another option
is to use a more automated approach, which could empirically evaluate the pre-
dictive performance of the recommender system on contextualized input datasets
obtained from each generalized pre-filter, and then would automatically choose
the pre-filter with best performance. An interesting and important research issue
is how to deal with potential computational complexity of this approach due to con-
text granularity; in other words, in cases of applications with highly granular con-
texts, there may exist a very large number of possible context generalizations, for
which exhaustive search techniques would not be practical. For such cases, effec-
tive greedy approaches would need to be developed. Among the related work, Jiang

20 Gediminas Adomavicius, Alexander Tuzhilin
and Tuzhilin [38] examine optimal levels of granularity of customer segments in
order to maximize predictive performance of segmentation methods. Applicability
of these techniques in the context-aware recommender systems settings constitutes
an interesting problem for future research.
Also note that the reduction-based approach is related to the problems of build-
ing local models in machine learning and data mining [10]. Rather than building the
global rating estimation model utilizing all the available ratings, the reduction-based
approach builds a local rating estimation model that uses only the ratings pertaining
to the user-specified criteria in which a recommendation is made (e.g., morning).
It is important to know if a local model generated by the reduction-based approach
outperforms the global model of the traditional 2D technique, where all the infor-
mation associated with the contextual dimensions is simply ignored. For example,
it is possible that it is better to use the contextual pre-filtering to recommend movies
to see in the movie theaters on weekends, but use the traditional 2D technique for
movies to see at home on VCRs. This is the case because the reduction-based ap-
proach, on the one hand, focuses recommendations on a particular segment and
builds a local prediction model for this segment, but, on the other hand, computes
these recommendations based on a smaller number of points limited to the consid-
ered segment. This tradeoff between having more relevant data for calculating an
unknown rating based only on the ratings with the same or similar context and hav-
ing fewer data points used in this calculation belonging to a particular segment (i.e.,
the sparsity effect) explains why the reduction-based recommendation method can
outperform traditional 2D recommendation techniques on some segments and un-
derperform on others. Which of these two trends dominates on a particular segment
may depend on the application domain and on the specifics of the available data.
Based on this observation, Adomavicius et al. [3] propose to combine a number of
contextual pre-filters with the traditional 2D technique (i.e., as a default filter, where
no filtering is done); this approach will be described as a case study in Section 4.
Among some recent developments, Ahn et al. [8] use a technique similar to the
contextual pre-filtering to recommend advertisements to mobile users by taking into
account user location, interest, and time, and Lombardi et al. [48] evaluate the ef-
fect of contextual information using a pre-filtering approach on the data obtained
from an online retailer. Also, Baltrunas and Ricci [15] take a somewhat different
approach to contextual pre-filtering in proposing and evaluating the benefits of the
item splitting technique, where each item is split into several fictitious items based
on the different contexts in which these items can be consumed. Similarly to the item
splitting idea, Baltrunas and Amatriain [14] introduce the idea of micro-profiling (or
user splitting), which splits the user profile into several (possibly overlapping) sub-
profiles, each representing the given user in a particular context. The predictions
are done using these contextual micro-profiles instead of a single user model. Note
that these data construction techniques fit well under the contextual pre-filtering
paradigm, because they are following the same basic idea (as the data filtering tech-
niques described earlier) – using contextual information to reduce the problem of
multidimensional recommendations to the standard 2D User × Item space, which
then allows to use any traditional recommendation techniques for rating prediction.

Context-Aware Recommender Systems 21
3.2 Contextual Post-Filtering
As shown in Figure 4b, the contextual post-filtering approach ignores context in-
formation in the input data when generating recommendations, i.e., when generat-
ing the ranked list of all candidate items from which any number of top-N recom-
mendations can be made, depending on specific values of N. Then, the contextual
post-filtering approach adjusts the obtained recommendation list for each user using
contextual information. The recommendation list adjustments can be made by:
• Filtering out recommendations that are irrelevant (in a given context), or
• Adjusting the ranking of recommendations on the list (based on a given context).
For example, in a movie recommendation application, if a person wants to see
a movie on a weekend, and on weekends she only watches comedies, the system
can filter out all non-comedies from the recommended movie list. More generally,
the basic idea for contextual post-filtering approaches is to analyze the contextual
preference data for a given user in a given context to find specific item usage pat-
terns (e.g., user Jane Doe watches only comedies on weekends) and then use these
patterns to adjust the item list, resulting in more “contextual” recommendations, as
depicted in Figure 5.
Fig. 5 Final phase of the contextual post-filtering approach: recommendation list adjustment.
As with many recommendation techniques, the contextual post-filtering ap-
proaches can be classified into heuristic and model-based techniques. Heuristic
post-filtering approaches focus on finding common item characteristics (attributes)
for a given user in a given context (e.g., preferred actors to watch in a given context),
and then use these attributes to adjust the recommendations, including:
• Filtering out recommended items that do not have a significant number of these
characteristics (e.g., to be recommended, the movies must have at least two of
the preferred actors in a given context), or
• Ranking recommended items based on howmany of these relevant characteristics
they have (e.g., the movies that star more of the user’s preferred actors in a given
context will be ranked higher).

22 Gediminas Adomavicius, Alexander Tuzhilin
In contrast, model-based post-filtering approaches can build predictive models
that calculate the probability with which the user chooses a certain type of item in
a given context, i.e., probability of relevance (e.g., likelihood of choosing movies
of a certain genre in a given context), and then use this probability to adjust the
recommendations, including:
• Filtering out recommended items that have the probability of relevance smaller
than a pre-defined minimal threshold (e.g., remove movies of genres that have a
low likelihood of being picked), or
• Ranking recommended items by weighting the predicted rating with the proba-
bility of relevance.
Panniello et al. [54] provide an experimental comparison of the exact pre-filtering
method (discussed in Section 3.1) versus two different post-filtering methods –
Weight and Filter – using several real-world e-commerce datasets. The Weight post-
filtering method reorders the recommended items by weighting the predicted rating
with the probability of relevance in that specific context, and the Filter post-filtering
method filters out recommended items that have small probability of relevance in
the specific context. Interestingly, the empirical results show that the Weight post-
filtering method dominates the exact pre-filtering, which in turn dominates the Filter
post-filtering method, thus, indicating that the best approach to use (pre- or post-
filtering) really depends on a given application.
As was the case with the contextual pre-filtering approach, a major advantage of
the contextual post-filtering approach is that it allows using any of the numerous tra-
ditional recommendation techniques previously proposed in the literature [5]. Also,
similarly to the contextual pre-filtering approaches, incorporating context general-
ization techniques into post-filtering techniques constitutes an interesting issue for
future research.
3.3 Contextual Modeling
As shown in Figure 4c, the contextual modeling approach uses contextual informa-
tion directly in the recommendation function as an explicit predictor of a user’s rat-
ing for an item. While contextual pre-filtering and post-filtering approaches can use
traditional 2D recommendation functions, the contextual modeling approach gives
rise to truly multidimensional recommendation functions, which essentially repre-
sent predictive models (built using decision tree, regression, probabilistic model, or
other technique) or heuristic calculations that incorporate contextual information in
addition to the user and item data, i.e., Rating = R(User, Item,Context). A signif-
icant number of recommendation algorithms – based on a variety of heuristics as
well as predictive modeling techniques – have been developed over the last 10-15
years, and some of these techniques can be extended from the 2D to the multidimen-
sional recommendation settings. We present a few examples of multidimensional

Context-Aware Recommender Systems 23
heuristic-based and model-based approaches for contextual modeling [4] in the rest
of this section.
3.3.1 Heuristic-Based Approaches
The traditional two-dimensional (2D) neighborhood-based approach [20, 61] can be
extended to the multidimensional case, which includes the contextual information,
in a straightforward manner by using an n-dimensional distance metric instead of
the user-user or item-item similarity metrics traditionally used in such techniques.
To see how this is done, consider an example of theUser× Item×Time recommen-
dation space. Following the traditional nearest neighbor heuristic that is based on
the weighted sum of relevant ratings, the prediction of a specific rating ru,i,t in this
example can be expressed as (see [4] for additional details):
ru,i,t = k
∑
(u′,i′,t ′)6=(u,i,t)
W ((u, i, t),(u′, i′, t ′))× ru′,i′,t ′ ,
whereW ((u, i, t),(u′, i′, t ′)) describes the “weight” rating ru′,i′,t ′ carries in the predic-
tion of ru,i,t , and k is a normalizing factor. Weight W ((u, i, t),(u′, i′, t ′)) is typically
inversely related to the distance between points (u, i, t) and (u′, i′, t ′) in multidimen-
sional space, i.e., dist[(u, i, t),(u′, i′, t ′)]. In other words, the closer the two points are
(i.e., the smaller the distance between them), the more weight ru′,i′,t ′ carries in the
weighted sum. One example of such relationship would be W ((u, i, t),(u′, i′, t ′)) =
1/dist[(u, i, t),(u′, i′, t ′)], but many alternative specifications are also possible. As
before, the choice of the distance metric dist is likely to depend on a specific applica-
tion. One of the simplest ways to define a multidimensional dist function is by using
the reduction-like approach (somewhat similar to the one described in Section 3.1),
by taking into account only the points with the same contextual information, i.e.,
dist[(u, i, t),(u′, i′, t ′)] =
{
dist[(u, i),(u′, i′)], if t = t ′
+∞, otherwise
This distance function makes ru,i,t depend only on the ratings from the segment
of points having the same values of time t. Therefore, this case is reduced to the stan-
dard 2-dimensional rating estimation on the segment of ratings having the same con-
text t as point (u, i, t). Furthermore, if we further refine function dist[(u, i),(u′, i′)]
in so that it depends only on the distance between users when i = i′, then we would
obtain a method that is similar to the pre-filtering approach described earlier. More-
over this approach easily extends to an arbitrary n-dimensional case by setting the
distance d between two rating points to dist[(u, i),(u′, i′)] if and only if the contexts
of these two points are the same.
Other ways to define the distance function would be to use the weighted Manhat-
tan distance metric, i.e.,
dist[(u, i, t),(u′, i′, t ′)] = w1d1(u,u′)+w2d2(i, i′)+w3d3(t, t ′),

24 Gediminas Adomavicius, Alexander Tuzhilin
or the weighted Euclidean distance metric, i.e.,
dist[(u, i, t),(u′, i′, t ′)] =
√
w1d21(u,u′)+w2d22(i, i′)+w3d23(t, t ′)
where d1,d2, and d3 are distance functions defined for dimensions User, Item, and
Time respectively, and w1,w2, and w3 are the weights assigned for each of these
dimensions (e.g., according to their importance). In summary, distance function
dist[(u, i, t),(u′, i′, t ′)] can be defined in many different ways and, while in many
systems it is typically computed between ratings of the same user or of the same
item, it constitutes an interesting research problem to identify various more general
ways to define this distance and compare these different ways in terms of predictive
performance.
3.3.2 Model-Based Approaches
There have been several model-based recommendation techniques proposed in rec-
ommender systems literature for the traditional two-dimensional recommendation
model [5]. Some of these methods can be directly extended to the multidimensional
case, such as the method proposed in [12], who show that their 2D technique out-
performs some of the previously known collaborative filtering methods.
The method proposed by Ansari et al. [12] combines the information about users
and items into a single hierarchical regression-based Bayesian preference model that
uses Markov Chain Monte Carlo (MCMC) techniques to estimate its parameters.
Specifically, let’s assume that there are NU users, where each user u is defined by
vector zu of observed attributes of user u, such as gender, age, and income. Also
assume that there are NI items, where each item i is defined by vector wi of attributes
of the item, such as price, weight, and size. Let rui be the rating assigned to item i by
user u, where rui is a real-valued number. Moreover, ratings rui are only known for
some subset of all possible (user, item) pairs. Then the unknown rating estimation
problem is defined as
rui = x′uiµ + z′uγi +w′iλu + eui, eui ∼ N(0,σ2), λu ∼ N(0,Λ), γi ∼ N(0,Γ )
where observed (known) values of the model are ratings rui assigned by user u for
item i, user attributes zu, item attributes wi, and vector xui = zu
⊗
wi, where
⊗
is the
Kronecker product, i.e., a long vector containing all possible cross-product combi-
nations between individual elements of zu and wi. Intuitively, this equation presents
a regression model specifying unknown ratings rui in terms of the characteristics
zu of user u, the characteristics wi of item i, and the interaction effects xui between
them. Interaction effects arise from the hierarchical structure of the model and are
intended to capture effects such as how the age of a user changes his or her pref-
erences for certain genres of movies. Vector µ in the above equation represents
unobserved (unknown) slope of the regression line, i.e., unknown coefficients in
the regression model that need to be estimated, as discussed below. Vector γi rep-

Context-Aware Recommender Systems 25
resents weight coefficients specific to item i that determine idiosyncrasy of item i,
i.e., the unobserved heterogeneity of item i that are not explicitly recorded in the
item profiles, such as direction, music and acting for the movies. Similarly, vector
λu represents weight coefficients specific to user u that determine idiosyncrasy of
that user, i.e., the unobserved user effects (heterogeneity) of user u. Finally, the error
term eui is normally distributed with mean zero and standard deviation σ . Further,
we consider a hierarchical model and assume that regression parameters γi and λu
are normally distributed as γi ∼ N(0,Γ ) and λu ∼ N(0,Λ), where Γ and Λ are un-
known covariance matrices. The parameters of the model are µ,σ2,Λ , and Γ . They
are estimated from the data containing the already known ratings using the MCMC
methods described in [12].
While the approach presented in [12] is described in the context of the traditional
two-dimensional recommender systems, it can be directly extended to include the
contextual information. For example, assume that we have a third dimension Time
that is defined by the following two attributes (variables): (a) Boolean variableweek-
end specifying whether a movie was seen on a weekend or not, and (b) a positive
integer variable numdays indicating the number of days after the release when the
movie was seen.
In such a case, the Ansari et al. [12] model can be extended to the third (Time)
dimension as in Adomavicius and Tuzhilin [4]:
ruit = x′uitµ + p′uiθt +q′itλu + r′tuγi + z′uδit +w′ipitu + y′tσui + euit
where euit ∼N(0,σ2), γi ∼N(0,Γ ),λu ∼N(0,Λ),θt ∼N(0,Θ),δit ∼N(0,∆),pitu ∼
N(0,Π), and σui ∼ N(0,Σ).
This model encompasses the effects of observed and unobserved user-, item- and
temporal-variables and their interactions on rating ruit of user u for movie i seen at
time t. The variables zu, wi and yt stand for the observed attributes of users (e.g.,
demographics), movies (e.g., genre) and time dimension (e.g., weekend, numdays).
The vector xuit represents the interaction effects of the user, movies, and time vari-
ables, and its coefficient µ represents the unobserved (unknown) slope of the re-
gression line, i.e., unknown coefficients in the above regression model that need to
be estimated, as discussed below. The vectors λu, γi and θt are random effects that
stand for the unobserved sources of heterogeneity of users (e.g., their ethnic back-
ground), movies (e.g., the story, screenplay, etc.) and temporal effects (e.g., was the
movie seen on a holiday or not, the season when it was released, etc). The vector
pui represents the interaction of the observed user and item variables, and likewise
qit and rtu. The vector σui represents the interaction of the unobserved user and item
attributes, and vectors pitu and δit have similar types of interactions. Finally, the pa-
rameters µ,σ2,Λ ,Γ ,Θ ,∆ ,Π , and Σ of the model can be estimated from the data of
the already known ratings using Markov Chain Monte Carlo (MCMC) methods, as
was done in [12].
Finally, note that the number of parameters in the above model that would need
to be estimated rises with the number of dimensions and, therefore, the sparsity of
known ratings may become a problem. If this is a serious problem for a particular

26 Gediminas Adomavicius, Alexander Tuzhilin
application, then some of the terms in the above model can be dropped, leading to
a simplified model. For example, we may decide to drop the term qitλu, or perhaps
some other terms, which would lead to a simpler model with fewer parameters. Note
that this simpler model would still take into account the contextual information,
e.g., time in this case. Furthermore, parameter estimation of this model can be very
time consuming and not scalable. Therefore, one of the research challenges is to
make such models more scalable and more robust in terms of the more accurate
estimations of unknown ratings. Some of the initial ideas of how to make these
approaches more scalable are presented in [66].
In addition to possible extensions of existing 2D recommendation techniques
to multiple dimensions, there have also been some new techniques developed
specifically for context-aware recommender systems based on the context model-
ing paradigm. For example, following the general contextual modeling paradigm,
Oku et al. [52] propose to incorporate additional contextual dimensions (such as
time, companion, and weather) directly into recommendation space and use ma-
chine learning technique to provide recommendations in a restaurant recommender
system. In particular, they use support vector machine (SVM) classification method,
which views the set of liked items and the set of disliked items of a user in various
contexts as two sets of vectors in an n-dimensional space, and constructs a sepa-
rating hyperplane in this space, which maximizes the separation between the two
data sets. The resulting hyperplane represents a classifier for future recommenda-
tion decisions (i.e., a given item in a specific context will be recommended if it falls
on the “like” side of the hyperplane, and will not be recommended if it falls on the
“dislike” side). Furthermore, Oku et al. [52] empirically show that context-aware
SVM significantly outperforms non-contextual SVM-based recommendation algo-
rithm in terms of predictive accuracy and user’s satisfaction with recommendations.
Similarly, Yu et al. [71] use contextual modeling approach to provide content rec-
ommendations for smart phone users by introducing context as additional model di-
mensions and using hybrid recommendation technique (synthesizing content-based,
Bayesian-classifier, and rule-based methods) to generate recommendations.
Finally, another model-based approach is presented in [1] where a Personalized
Access Model (PAM) is presented that provides a set of personalized context-based
services, including context discovery, contextualization, binding and matching ser-
vices. Then Abbar et al. [1] describe how these services can be combined to form
Context-Aware Recommender Systems (CARS) and deployed in order to provide
superior context-aware recommendations.
In this section we described various ways to incorporate contextual informa-
tion into recommendation algorithms within the framework of pre-filtering, post-
filtering, and contextual modeling methods. Since CARS is a new and an emerging
area of recommender systems, the presented methods constitute only the initial ap-
proaches to providing recommendations, and better-performing methods need and
should be developed across all these three approaches.
In the next section, we discuss how these different individual methods can be
combined together into one common approach.

Context-Aware Recommender Systems 27
4 Combining Multiple Approaches
As has been well-documented in recommender systems literature, often a combi-
nation (a “blend” or an ensemble) of several solutions provides significant perfor-
mance improvements over the individual approaches [23, 24, 42, 55]. The three
paradigms for context-aware recommender systems offer several different opportu-
nities for employing combined approaches.
One possibility is to develop and combine several models of the same type. For
example, Adomavicius et al. [3] followed this approach to develop a technique that
combines information from several different contextual pre-filters. The rationale for
having a number of different pre-filters is based on the fact that, as mentioned earlier,
typically there can be multiple different (and potentially relevant) generalizations of
the same specific context. For example, context c = (Girlfriend, Theater, Saturday)
can be generalized to c1 = (Friend, AnyPlace, Saturday), c2 = (NotAlone, Theater,
AnyTime), and a number of other contexts. Following this idea, Adomavicius et
al. [3] use pre-filters based on the number of possible contexts for each rating, and
then combine recommendations resulting from each contextual pre-filter. The gen-
eral overview of this approach is shown in Figure 6. Note that the combination of
several pre-filters can be done in multiple ways. For example, for a given context,
(a) one could choose the best-performing pre-filter, or (b) use an “ensemble” of pre-
filters. In the remainder of Section 4, we will discuss the approach developed by
Adomavicius et al. [3] in more detail as a case study.
Fig. 6 Combining multiple pre-filters: an overview.
Another interesting possibility stems from an observation that complex contex-
tual information can be split into several components, and the utility of each piece of
contextual information may be different depending on whether it is used in the pre-
filtering, post-filtering, or modeling stage. For example, time information (weekday

28 Gediminas Adomavicius, Alexander Tuzhilin
vs. weekend) may be most useful to pre-filter relevant data, but weather information
(sunny vs. rainy) may be the most appropriate to use as a post-filter. Determining
the utility of different contextual information with respect to different paradigms of
context-aware recommender systems constitutes an interesting and promising direc-
tion for future research.
4.1 Case Study of Combining Multiple Pre-Filters: Algorithms
The combined approach to rating estimation consists of two phases [3]: (i) using
known user-specified ratings (i.e., training data), determine the use of which pre-
filters outperforms the traditional CF method; (ii) in order to predict a specific rating
in a given context, choose the best pre-filter for that particular context and use the
two-dimensional recommendation algorithm on this segment.
The first phase is a pre-processing phase and is usually performed “offline.” It
can work with any traditional 2D rating estimation method A, and consists of the
following three steps [3]:
1. Among all possible generalized pre-filters, find the ones which result in contex-
tual segments having a significantly large amount of data, i.e., segments with
more than N ratings, where N is some predetermined threshold (e.g., N = 250
was used in that study). If the recommendation space is “small” (in the number
of dimensions and the ranges of attributes in each dimension), the large segments
can be obtained simply by doing an exhaustive search in the space of all possible
segments. Alternatively, the help of a domain expert (e.g., a marketing manager)
or some greedy heuristics could be used to determine the important large seg-
ments for the application.
2. For each generalized pre-filter c′ determined in Step 1, algorithm A is run using
this pre-filter and its predictive performance is determined using a chosen perfor-
mance metric (this study used F-measure, which is defined as a harmonic mean
of Precision and Recall – two standard decision support metrics widely used in
information retrieval and, more recently, in recommender systems [36]). Only
those pre-filters are kept, where the performance of algorithm A on contextually
pre-filtered inputs exceeds the performance of the standard, i.e., non-filtered, ver-
sion of algorithm A on that same data segment.
3. Among the remaining pre-filters, if there exist pre-filters c′ and c′′ such that
c′ → c′′ (i.e., c′′ is strictly more general than c′) and algorithm A demonstrates
better performance using pre-filter c′′ than pre-filter c′, then c′ is deemed to be
redundant (less general and less accurate) and is removed from the set of pre-
filters. The set of remaining contextual segments, denoted SEGM∗, constitutes
the result of the “offline” phase of the combined approach.
Once the set of high-performing pre-filters SEGM∗ is computed, we can per-
form the second phase of the combined approach and determine which pre-filter to
use in “real-time” when an actual recommendation needs to be produced. Given the

Context-Aware Recommender Systems 29
specific context c of recommendation, the best-performing pre-filter c′ ∈ SEGM∗
such that c → c′ or c = c′ is used in conjunction with algorithm A. If no such pre-
filter c′ exists, then the standard 2D non-filtered algorithm A (i.e., trained on the
entire dataset) is used for rating prediction.
The main advantage of the combined pre-filtering approach described in this sec-
tion is that it uses the contextual pre-filters only for those contextual situations where
this method outperforms the standard 2D recommendation algorithm, and continues
to use the latter where there is no improvement. Therefore, the combined approach
is expected to perform equally well or better than the pure 2D approach in prac-
tice. The extent to which the combined approach can outperform the 2D approach
depends on many different factors, such as the problem domain or quality of data.
4.2 Case Study of Combining Multiple Pre-Filters: Experimental
Results
To illustrate how the combined approach presented in Section 4.1 performs in prac-
tice, it was evaluated on a real-world movie recommendation application and com-
pared its performance with the traditional 2D CF method [3]. In this application, in
addition to being asked to rate their movie-going experience, the users were asked
to specify: (a) time when the movie was seen (choices: weekday, weekend, don’t
remember); furthermore, if seen on a weekend, was it the opening weekend for
the movie (choices: yes, no, don’t remember); (b) place where the movie was seen
(choices: in a movie theater, at home, don’t remember); and (c) companion with
whom the movie was seen (choices: alone, with friends, with boyfriend/girlfriend,
with family or others). Overall, 1755 ratings were entered by 117 students over a
period of 12 months (May’01-Apr’02). Since some students rated very few movies,
those students with fewer than 10 ratings were dropped in our analysis. Therefore,
from an initial data, the final dataset had only 62 students, 202 movies and 1457
total ratings.
During the first step of the “offline”, pre-filter selection phase, 9 large contextual
segments were discovered, as presented in Table 1. However, after comparing the
performance of the standard CF technique and the contextual pre-filtering CF tech-
nique on each of these segments (i.e., second step of the pre-filter selection phase),
only 4 “high-performing” segments were found; one of them was subsequently re-
moved after the redundancy check (i.e., third step of the pre-filter selection phase,
as described in Section 4.1). The remaining set of 3 high-performing pre-filters is
shown in Table 2, i.e., SEGM∗ = {Theater-Weekend, Theater, Weekend}.
Finally, the resulting high-performing segments SEGM∗ were used in the “on-
line”, rating estimation phase. Overall, there were 1373 (of the 1457 collected) rat-
ings that both 2D CF and the combined pre-filtering CF approaches were able to
predict (not all ratings were predicted because of the sparsity-related limitations of
data). The results show that the combined pre-filtering CF approach substantially
outperformed the traditional 2D CF (1st row in Table 3).

30 Gediminas Adomavicius, Alexander Tuzhilin
Table 1 Large contextual segments generated in Step 1 of the pre-filter selection algorithm.
Name Size Description
Home 727 Movies watched at home
Friends 565 Movies watched with friends
NonRelease 551 Movies watched on other than the opening weekend
Weekend 538 Movies watched on weekends
Theater 526 Movies watched in the movie theater
Weekday 340 Movies watched on weekdays
GBFriend 319 Movies watched with girlfriend/boyfriend
Theater-Weekend 301 Movies watched in the movie theater on weekends
Theater-Friends 274 Movies watched in the movie theater with friends
Table 2 High-performing large contextual segments.
Segment CF: Segment-trained F-measure CF: Whole-data-trained F-measure
Theater-Weekend 0.641 0.528
Theater 0.608 0.479
Weekend 0.542 0.484
Note that, as discussed earlier, the combined pre-filtering CF approach incorpo-
rates the standard CF approach, since it would use the standard 2D CF to predict the
value of any rating that does not belong to any of the discovered high-performing
pre-filters. Consequently, in this application, the predictions of the two approaches
are identical for all ratings that do not belong to any segment in {Theater-Weekend,
Theater, Weekend}. Since such ratings do not contribute to the differentiation be-
tween the two approaches, it is important to determine how well the two approaches
do on the ratings from SEGM∗. In this case, there were 743 such ratings in SEGM∗
(out of 1373), and the difference in F-measure performance of the two approaches
is 0.095 (2nd row in Table 3), which is even more substantial than for the previously
described case.
Table 3 Overall comparison based on F-measure.
Comparison Overall F-measure Difference between F-measures
Standard 2D CF Combined reduction-
based CF
All predictions
(1373 ratings) 0.463 0.526 0.063
Predictions on
ratings from SEGM∗
(743 ratings) 0.450 0.545 0.095

Context-Aware Recommender Systems 31
In this section, we discussed combining multiple pre-filtering, post-filtering, and
contextual modeling methods to generate better predictions, focusing primarily on
combining multiple pre-filters, based on [3]. We outlined only some of the main
ideas, while leaving most of the problems in this area as wide open and subject of
future research. We believe that creative combinations of multiple methods using
ensemble techniques from machine learning can significantly increase performance
of CARS and constitute an important and interesting area of research.
5 Additional Issues in Context-Aware Recommender Systems
In addition to the three paradigms of incorporating context in recommender systems
and the methods of combining these three paradigms, there are several other im-
portant topics in context-aware recommender systems, such as how to better utilize
contextual information, how to develop richer interaction capabilities with CARS
that make recommendations more flexible, and how to build high-performing CARS
systems. We discuss these issues in the rest of this section.
Studying Tradeoffs Between Pre-, Post-Filtering, and Contextual Modeling Ap-
proaches and Developing Better Understanding of How to Combine Them. In Sec-
tion 3, we only described three types of general CARS paradigms and did not
consider tradeoffs between them. In order to achieve better understanding of pre-
filtering, post-filtering, and contextual modeling approaches, it is important to con-
duct studies comparing all the three approaches and identify relative advantages
and disadvantages of these methods. One such study is described in [54], where
a pre-filtering method is compared to a particular type of post-filtering method in
terms of the quality of recommendations. It was shown that neither method dom-
inated the other in terms of providing better recommendations, as measured using
the F-measure. Furthermore, Panniello et al. [54] proposed a procedure identifying
a set of conditions under which the pre-filtering method should be used vis-a`-vis the
post-filtering methods.
The work reported in [54] constitutes only the first step towards a systematic pro-
gram of comparing the pre-filtering, post-filtering, and contextual modeling meth-
ods, and much more work is required to develop comprehensive understanding of
the relative merits of each of the three methods and to understand which methods
perform better than others and under which conditions.
Similarly, more work is required to better understand the combined approach
discussed in Section 4. This is a fruitful area of research that is open to numerous
improvements and important advances including various types of deployments of
ensemble methods combining the pre- and post-filtering as well contextual model-
ing approaches.

32 Gediminas Adomavicius, Alexander Tuzhilin
Developing Richer Interaction and More Flexible Recommendation Capabilities
of CARS. Context-aware recommendations have the following two important prop-
erties:
• Complexity. Since CARS involve not only users and items in the recommenda-
tion process, but also various types of contextual information, the types of such
recommendations can be significantly more complex in comparison to the tra-
ditional non-contextual cases. For example, in a movie recommendation appli-
cation, a certain user (e.g., Tom) may seek recommendations for him and his
girlfriend of top 3 movies and the best times to see them over the weekend.
• Interactivity. The contextual information usually needs to be elicited from the
user in the CARS settings. For example, to utilize the available contextual infor-
mation, a CARS system may need to elicit from the user (Tom) with whom he
wants to see a movie (e.g., girlfriend) and when (e.g., over the weekend) before
providing any context-specific recommendations.
The combination of these two features calls for the development of more flexible
recommendation methods that allow the user to express the types of recommenda-
tions that are of interest to them rather than being “hard-wired” into the recommen-
dation engines provided by most of the current vendors that, primarily, focus on
recommending top-N items to the user and vice versa. The second requirement of
interactivity also calls for the development of tools allowing users to provide inputs
into the recommendation process in an interactive and iterative manner, preferably
via some well-defined user interface (UI).
Such flexible context-aware recommendations can be supported in several ways.
First, Adomavicius et al. [6] developed a recommendation query language RE-
QUEST4 that allows its users to express in a flexible manner a broad range of rec-
ommendations that are tailored to their own individual needs and, therefore, more
accurately reflect their interests. REQUEST is based on the multidimensional con-
textual recommendation model described in Section 2.2 and also in [3]. REQUEST
supports a wide variety of features, and the interested reader can find the detailed
account of these features as well as the formal syntax and various properties of
the language in [6]. In addition, Adomavicius et al. [6] provide a discussion of the
expressive power of REQUEST and present a multidimensional recommendation
algebra that provides the theoretical basis for this language.
So far, we have briefly mentioned only the recommendation query language it-
self. Since a major argument for introducing such a language is its use by the end-
users, it is also very important to develop simple, friendly, and expressive user in-
terfaces (UIs) for supporting flexible but sometimes complex contextual recommen-
dations. High-quality UIs should reduce the complexity and simplify interactions
between the end-users and the recommender system and make them available to
wider audiences. Developing such UIs constitutes a topic of future research.
Another proposal to provide flexible recommendations is presented in [43],
where the FlexRecs system and framework are described. FlexRecs approach sup-
ports flexible recommendations over structured data by decoupling the definition of
4 REQUEST is an acronym for REcommendation QUEry STatements.

Context-Aware Recommender Systems 33
a recommendation process from its execution. In particular, a recommendation can
be expressed declaratively as a high-level parameterized workflow containing tra-
ditional relational operators and novel recommendation-specific operators that are
combined together into a recommendation workflow.
In addition to developing languages for expressing context-aware recommenda-
tions, it is also important to provide appropriate user interfaces so that the users were
able to express flexible recommendations in an interactive manner. For FlexRecs,
this entails building a UI for defining and managing recommendation workflows,
and for REQUEST this entails providing front-end UI allowing users to express
REQUEST queries using visual and interactive methods.
Developing High-Performing CARS Systems and Testing Them on Practical Ap-
plications. Most of the work on context-aware recommender systems has been con-
ceptual, where a certain method has been developed, tested on some (often limited)
data, and shown to perform well in comparison to certain benchmarks. There has
been little work done on developing novel data structures, efficient storage meth-
ods, and new systems architectures for CARS. One example of such work is the
paper by Hussein et al. [37], where the authors introduce a service-oriented archi-
tecture enabling to define and implement a variety of different “building blocks”
for context-aware recommender systems, such as recommendation algorithms, con-
text sensors, various filters and converters, in a modular fashion. These building
blocks can then be combined and reused in many different ways into systems that
can generate contextual recommendations. Another example of such work is Abbar
et al. [1], where the authors present a service-oriented approach that implements
the Personalized Access Model (PAM) previously proposed by the authors. The im-
plementation is done using global software architecture of CARS developed by the
authors and described in [1]. These two papers constitute only the initial steps to-
wards developing better understanding of how to build more user-friendly, scalable,
and better performing CARS systems, and much more work is required to achieve
this goal.
6 Conclusions
In this chapter we argued that relevant contextual information does matter in rec-
ommender systems and that it is important to take this contextual information into
account when providing recommendations. We also explained that the contextual
information can be utilized at various stages of the recommendation process, in-
cluding at the pre-filtering and the post-filtering stages and also as an integral part
of the contextual modeling. We have also showed that various techniques of using
the contextual information, including these three methods, can be combined into
a single recommendation approach, and we presented a case study describing one
possible way of such combining.

34 Gediminas Adomavicius, Alexander Tuzhilin
Overall, the field of context-aware recommender systems (CARS) is a relatively
new and underexplored area of research, and much more work is needed to explore
it comprehensively. We provided suggestions of several possible future research di-
rections that were presented throughout the paper. In conclusion, CARS constitutes
a newly developing and promising research area with many interesting and practi-
cally important research problems.
Acknowledgements Research of G. Adomavicius was supported in part by the National Science
Foundation grant IIS-0546443, USA. The authors thank YoungOk Kwon for editorial assistance.
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