Toward the Next Generation of Recommender
Systems: A Survey of the State-of-the-Art and
Possible Extensions
Gediminas Adomavicius, Member, IEEE, and Alexander Tuzhilin, Member, IEEE
Abstract—This paper presents an overview of the field of recommender systems and describes the current generation of
recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid
recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses
possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader
range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of
the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less
intrusive types of recommendations.
Index Terms—Recommender systems, collaborative filtering, rating estimation methods, extensions to recommender systems.


1 INTRODUCTION
RECOMMENDER systems have become an importantresearch area since the appearance of the first papers
on collaborative filtering in the mid-1990s [45], [86], [97].
There has been much work done both in the industry and
academia on developing new approaches to recommender
systems over the last decade. The interest in this area still
remains high because it constitutes a problem-rich
research area and because of the abundance of practical
applications that help users to deal with information
overload and provide personalized recommendations,
content, and services to them. Examples of such applica-
tions include recommending books, CDs, and other
products at Amazon.com [61], movies by MovieLens
[67], and news at VERSIFI Technologies (formerly
AdaptiveInfo.com) [14]. Moreover, some of the vendors
have incorporated recommendation capabilities into their
commerce servers [78].
However, despite all of these advances, the current
generation of recommender systems still requires further
improvements to make recommendation methods more
effective and applicable to an even broader range of real-life
applications, including recommending vacations, certain
types of financial services to investors, and products to
purchase in a store made by a “smart” shopping cart [106].
These improvements include better methods for represent-
ing user behavior and the information about the items to be
recommended, more advanced recommendation modeling
methods, incorporation of various contextual information
into the recommendation process, utilization of multcriteria
ratings, development of less intrusive and more flexible
recommendation methods that also rely on the measures
that more effectively determine performance of recommen-
der systems.
In this paper, we describe various ways to extend the
capabilities of recommender systems. However, before
doing this, we first present a comprehensive survey of the
state-of-the-art in recommender systems in Section 2. Then,
we identify various limitations of the current generation of
recommendation methods and discuss some initial ap-
proaches to extending their capabilities in Section 3.
2 THE SURVEY OF RECOMMENDER SYSTEMS
Although the roots of recommender systems can be traced
back to the extensive work in cognitive science [87],
approximation theory [81], information retrieval [89],
forecasting theories [6], and also have links to management
science [71] and to consumer choice modeling in marketing
[60], recommender systems emerged as an independent
research area in the mid-1990s when researchers started
focusing on recommendation problems that explicitly rely
on the ratings structure. 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. Intuitively, this estimation is usually based on the
ratings given by this user to other items and on some other
information that will be formally described below. Once we
can estimate ratings for the yet unrated items, we can
recommend to the user the item(s) with the highest
estimated rating(s).
More formally, the recommendation problem can be
formulated as follows: Let C be the set of all users and let S
be the set of all possible items that can be recommended,
such as books, movies, or restaurants. The space S of
734 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 6, JUNE 2005
. G. Adomavicius is with the Carlson School of Management, University of
Minnesota, 321 19th Avenue South, Minneapolis, MN 55455.
E-mail: gedas@umn.edu.
. A. Tuzhilin is with the Stern School of Business, New York University,
44 West 4th Street, New York, NY 10012. E-mail: atuzhili@stern.nyu.edu.
Manuscript received 8 Mar. 2004; revised 14 Oct. 2004; accepted 10 Dec.
2004; published online 20 Apr. 2005.
For information on obtaining reprints of this article, please send e-mail to:
tkde@computer.org, and reference IEEECS Log Number TKDE-0071-0304.
1041-4347/05/$20.00
2005 IEEE Published by the IEEE Computer Society