A Maximum Entropy Web Recommendation System : Combining Collaborative and Content Features

  • Jin X
  • Zhou Y
  • Mobasher B
  • 71

    Readers

    Mendeley users who have this article in their library.
  • 74

    Citations

    Citations of this article.

Abstract

Web users display their preferences implicitly by navigating through a sequence of pages or by providing numeric ratings to some items. Web usage mining techniques are used to extract useful knowledge about user interests from such data. The discovered user models are then used for a variety of applications such as personalized recommendations. Web site content or semantic features of objects provide another source of knowledge for deciphering users' needs or interests. We propose a novel Web recommendation system in which collaborative features such as navigation or rating data as well as the content features accessed by the users are seamlessly integrated under the maximum entropy principle. Both the discovered user patterns and the semantic relationships among Web objects are represented as sets of constraints that are integrated to fit the model. In the case of content features, we use a new approach based on Latent Dirichlet Allocation (LDA) to discover the hidden semantic relationships among items and derive constraints used in the model. Experiments on real Web site usage data sets show that this approach can achieve better recommendation accuracy, when compared to systems using only usage information. The integration of semantic information also allows for better interpretation of the generated recommendations.

Author-supplied keywords

  • maximum entropy
  • recommendation
  • web usage mining

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Xin Jin

  • Yanzan Zhou

  • Bamshad Mobasher

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free