ABSTRACT
Recommender systems strive to recommend items that users will appreciate and rate highly, often presenting items in order of highest predicted ratings first. In this working paper we present Eigentaste 5.0, a constant-time recommender system that dynamically adapts the order that items are recommended by integrating user clustering with item clustering and monitoring item portfolio effects. This extends our Eigentaste 2.0 algorithm, which uses principal component analysis to cluster users offline. In preliminary experiments we backtested Eigentaste 5.0 on data collected from Jester, our online joke recommender system. Results suggest that it will perform better than Eigentaste 2.0. The new algorithm also uses item clusters to address the cold-start problem for introducing new items.
- D. Cosley, S. Lam, I. Albert, J. Konstan, and J. Riedl. Is seeing believing?: how recommender system interfaces affect users' opinions. Proc. of the Annual SIGCHI Conf., pages 585--592, 2003. Google ScholarDigital Library
- M. Deshpande and G. Karypis. Item-based top-N recommendation algorithms. ACM TOIS, 22(1):143--177, 2004. Google ScholarDigital Library
- T. George and S. Merugu. A scalable collaborative filtering framework based on co-clustering. Proc. of the 5th IEEE Int'l Conf. on Data Mining, pages 625--628, 2005. Google ScholarDigital Library
- K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: a constant time collaborative filtering algorithm. Information Retrieval, 4(2):133--151, 2001. Google ScholarDigital Library
- J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM TOIS, 22(1):5--53, 2004. Google ScholarDigital Library
- D. Kim and B. Yum. Collaborative filtering based on iterative principal component analysis. Expert Systems with Applications, 28(4):823--830, 2005. Google ScholarDigital Library
- J. Konstan, S. McNee, C. Ziegler, R. Torres, N. Kapoor, and J. Riedl. Lessons on Applying Automated Recommender Systems to Information Seeking Tasks. Proceedings of the Twenty-First National Conference on Artificial Intelligence, 2006. Google ScholarDigital Library
- D. Lemire. Scale and translation invariant collaborative filtering systems. Information Retrieval, 8(1):129--150, 2005. Google ScholarDigital Library
- G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, 2003. Google ScholarDigital Library
- S. Park, D. Pennock, O. Madani, N. Good, and D. DeCoste. Naïve filterbots for robust cold-start recommendations. Proc. of the 12th ACM SIGKDD Int'l Conf., pages 699--705, 2006. Google ScholarDigital Library
- D. Pennock, E. Horvitz, S. Lawrence, and C. Giles. Collaborative filtering by personality diagnosis: a hybrid memory and model-based approach. Proc. of the 16th Conf. on Uncertainty in Artificial Intelligence, pages 473--480, 2000. Google ScholarDigital Library
- T. Quan, I. Fuyuki, and H. Shinichi. Improving accuracy of recommender system by clustering items based on stability of user similarity. IAWTIC'2006 Proc., pages 61--61, 2006. Google ScholarDigital Library
- A. Rashid, I. Albert, D. Cosley, S. Lam, S. McNee, J. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. Proc. of the 7th Int'l Conf. on Intelligent User Interfaces, pages 127--134, 2002. Google ScholarDigital Library
- A. Rashid, S. Lam, G. Karypis, and J. Riedl. ClustKNN: a highly scalable hybrid model & memory-based CF algorithm. Proc. of WebKDD 2006, 2006.Google Scholar
- A. Schein, A. Popescul, L. Ungar, and D. Pennock. Methods and metrics for cold-start recommendations. Proc. of the 25th Annual Int'l ACM SIGIR Conf., pages 253--260, 2002. Google ScholarDigital Library
- J. Thornton. Collaborative filtering research papers.Google Scholar
- M. Vozalis and K. Margaritis. On the combination of user-based and item-based collaborative filtering. International Journal of Computer Mathematics, 81(9):1077--1096, 2004.Google ScholarCross Ref
- J. Wang, A. de Vries, and M. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. Proc. of the 29th Annual Int'l ACM SIGIR Conf., pages 501--508, 2006. Google ScholarDigital Library
- D. Wilson, B. Smyth, and D. Sullivan. Sparsity reduction in collaborative recommendation: a casebased approach. Int'l Journal of Pattern Recognition and Artificial Intelligence, 17(5):863--884, 2003.Google ScholarCross Ref
- G. Xue, C. Lin, Q. Yang, W. Xi, H. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. Proc. of the 28th Annual Int'l ACM SIGIR Conf., pages 114--121, 2005. Google ScholarDigital Library
- C. Ziegler, S. McNee, J. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. Proceedings of the 14th international conference on World Wide Web, pages 22--32, 2005. Google ScholarDigital Library
Index Terms
- Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
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