Xin Jin
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.
- D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.]] Google Scholar
Digital Library
- J. Goodman. Sequential conditional generalized iterative scaling. In Proceedings of NAACL-2002, 2002.]] Google ScholarDigital Library
- F. Jelinek. Statistical Methods for Speech Recognition. MIT Press, MA, 1998.]] Google ScholarDigital Library
- J. Jeon and R. Manmatha. Using maximum entropy for automatic image annotation. In Proceedings of the International Conference on Image and Video Retrieval (CIVR-2004), 2004.]]Google ScholarCross Ref
- X. Jin, Y. Zhou, and B. Mobasher. A unified approach to personalization based on probabilistic latent semantic models of web usage and content. In Proceedings of the AAAI 2004 Workshop on Semantic Web Personalization (SWP'04), San Jose, CA, 2004.]]Google Scholar
- X. Jin, Y. Zhou, and B. Mobasher. Task-oriented web user modeling for recommendation. In Proceedings of the 10th International Conference on User Modeling (UM'05), UK, April 2005.]] Google ScholarDigital Library
- R. Malouf. A comparison of algorithms for maximum entropy parameter estimation. In Proceedings of the Sixth Conference on Natural Language Learning(2002), 2002.]] Google ScholarDigital Library
- B. Mobasher, H. Dai, T. Luo, Y. Sun, and J. Zhu. Integrating web usage and content mining for more effective personalization. In E-Commerce and Web Technologies: Proceedings of the EC-WEB 2000 Conference, Lecture Notes in Computer Science (LNCS) 1875, pages 165--176. Springer, September 2000.]] Google ScholarDigital Library
- M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths. Probabilistic author-topic models for information discovery. In Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2004.]] Google ScholarDigital Library
- K. Nigram, J. Lafferty, and A. McCallum. Using maximum entropy for text classification. In Proceedings of IJCAI-1999, 1999.]]Google Scholar
- D. Pavlov, E. Manavoglu, D. Pennock, and C. Giles. Collaborative filtering with maximum entropy. IEEE Intelligent Systems, Special Issue on Mining the Web Actionable Knowledge, 2004.]] Google ScholarDigital Library
- D. Pavlov and D. Pennock. A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. In Proceedings of Neural Information Processing Systems(2002), 2002.]]Google Scholar
- M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393--408, 1999.]] Google ScholarDigital Library
- A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of 17th UAI, Seattle, WA, 2001.]] Google ScholarDigital Library
- R. Rosenfeld. Adaptive statistical language modeling: A maximum entropy approach. Phd dissertation, CMU, 1994.]]Google Scholar
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International WWW Conference, Hong Kong, May 2001.]] Google ScholarDigital Library
- K. Yu, A. Schwaighofer, V. Tresp, W. Ma, and H. Zhang. Collaborative ensembling learning: Combining collaborative and content-based information filtering. In Proceedings of 19th UAI, 2003.]]Google Scholar
- C. Zitnick and T. Kanade. Maximum entropy for collaborative filtering. In Proceedings of 20th International Conference on Uncertainty in Artificial Intelligence (UAI'04), Banff, Canada, July 2004.]] Google ScholarDigital Library
Index Terms
- A maximum entropy web recommendation system: combining collaborative and content features
Recommendations
Web mining for web personalization
Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in ...
Personalization in tag ontology learning for recommendation making
IIWAS '12: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & ServicesDue to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing ...
NEWER: A system for NEuro-fuzzy WEb Recommendation
In the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by ...
Comments