ABSTRACT
Collaborative filtering is regarded as one of the most promising recommendation algorithms. The item-based approaches for collaborative filtering identify the similarity between two items by comparing users' ratings on them. In these approaches, ratings produced at different times are weighted equally. That is to say, changes in user purchase interest are not taken into consideration. For example, an item that was rated recently by a user should have a bigger impact on the prediction of future user behaviour than an item that was rated a long time ago. In this paper, we present a novel algorithm to compute the time weights for different items in a manner that will assign a decreasing weight to old data. More specifically, the users' purchase habits vary. Even the same user has quite different attitudes towards different items. Our proposed algorithm uses clustering to discriminate between different kinds of items. To each item cluster, we trace each user's purchase interest change and introduce a personalized decay factor according to the user own purchase behaviour. Empirical studies have shown that our new algorithm substantially improves the precision of item-based collaborative filtering without introducing higher order computational complexity.
- C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for projected clustering of high dimensional data streams. In VLDB, pages 852--863, 2004. Google ScholarDigital Library
- K. Ali and W. v. Stam. Tivo: making show recommendations using a distributed collaborative filtering architecture. In Conference on Knowledge Discovery in Data, pages 394--401, Seattle, WA, USA, 04. Google ScholarDigital Library
- J. S. Breese, D. Heekerman, and C. Kadic. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artficial Intelligence(UAI), 1998. Google ScholarDigital Library
- W. Fan. Systematic data selection to mine concept-drifting data streams. In Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, pages 128-- 137, Seattle, WA, USA, 2004. Google ScholarDigital Library
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. communications of the ACM, 35(12):61--70, 1992. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), Volume 22(Issue 1):5--53, 2004. Google ScholarDigital Library
- T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 259--266, Toronto, Canada, 2003. Google ScholarDigital Library
- R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In Annual ACM Conference on Research and Development in Information Retrieval, pages 337--344, 2004. Google ScholarDigital Library
- R. Jin and L. Si. A study of methods for normalizing user ratings in collaborative filtering. In Annual ACM Conference on Research and Development in Information Retrieval, pages 568--569, 2004. Google ScholarDigital Library
- M. L., C. N., and J. d. J. Perez-Alcazar. A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings. In ACM symposium on Applied computing, pages 1033--1039, 2004. Google ScholarDigital Library
- Q. Li and M. Zhou. Research and design of an efficient collaborative filtering predication algorithm. In Parallel and Distributed Computing, Applications and Technologies, 2003. PDCAT'2003, pages 171--174, 2003.Google Scholar
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In ACM conference on Computer supported cooperative work, pages 175--186, 1994. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In International World Wide Web Conference, pages 285--295, 2001. Google ScholarDigital Library
- L. Si and R. Jin. flexible mixture model for collaborative filtering. In the twentieth international conference on machine learning (ICML-2003), washington DC, 2003.Google Scholar
- K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of the 13th international conference on World Wide Web, pages 675--684, New York, NY, USA, 2004. Google ScholarDigital Library
- T. Y. Tang, P. Winoto, and K. C. C. Chan. On the temporal analysis for improved hybrid recommendations. In Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on, pages 214--220, 2003. Google ScholarDigital Library
- L. Terveen, J. McMackin, B. Amento, and W. Hill. Specifying preferences based on user history. In Conference on Human Factors in Computing Systems, pages 315--322, Minneapolis, Minnesota, USA, 2002. Google ScholarDigital Library
- H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 226--235, Washington, D.C, 2003. Google ScholarDigital Library
- C. Zeng, C.-X. Xing, and L.-Z. Zhou. Similarity measure and instance selection for collaborative filtering. In International World Wide Web Conference, pages 652--658, 2004. Google ScholarDigital Library
- Y. Zhao, C. Zhang, and S. Zhang. A recent-biased dimension reduction technique for time series data. In Advances in Knowledge Discovery and Data Mining: 9th Pacific-Asia Conference, PAKDD 2005, volume 3518 / 2005. Lecture Notes in Computer Science, 2005. Google ScholarDigital Library
- C.-N. Ziegler, G. Lausen, and L. S. Thieme. Taxonomy-driven computation of product recommendations. In the Thirteenth ACM conference on Information and knowledge management, pages 406--415, Washington, D.C., USA, 2004. Google ScholarDigital Library
Index Terms
- Time weight collaborative filtering
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