Abstract
Collaborative filtering (CF) algorithms predict interests of an active user in order to deal with the overload of information. Usually, changes of her interests have been ignored in traditional algorithms, which take user’s interest as static data and product rating in different phase with same weight. So when users’ interests have changed as time goes on, unneeded items may be recommended. In order to solve above problem, we propose a new item-based collaborative filtering algorithm in this paper. In this algorithm, named PFCF, we firstly divide users’ rating history into several periods, then users’ interests distributing in these periods are analyzed by a phrased forecast method, which is used to find user’s different type interests. The proposed algorithm is strictly tested on the MovieLens data set. The experimental results show its good precision against other traditional item-based collaborative filtering algorithms.
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References
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendatin Algorithms. In: International World Wide Web Conference, pp. 285–295 (2001)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)
Wikipedia. Recommender system (2009), http://en.wikipedia.org/wiki/Recommendation_system
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th annual conference on uncertainty in artificial intelligence, pp. 3–52 (1998)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Transactions on Internet Computing 7(1), 76–80 (2003)
Ding, Y., Li, X.: Time Weight Collaborative Filtering. In: Proc. of the 14th ACM international conference on Information and knowledge management, pp. 285–492 (2005)
Ding, Y., Li, X., Orlowska, M.E.: Recency-Based Collaborative Filtering. In: Proc. of the 17th Australasian Database Conference, vol. 49, pp. 99–107 (2006)
Xing, C.X., Gao, F.R., Zhan, S.N., Zhou, L.Z.: A Collaborative Filtering Recommendation Algorithms Incorporated with User Interest Change. Journal of Computer Research and Development 44(2), 296–301 (2007)
Zhao, J.G., Li, X.Y., Sun, J.Y.: TDCF: Time Distribution Collaborative Filtering Algorithm. In: International Symposium on Information Science and Engieering, pp. 98–101 (2008)
GroupLens Research (March 2009), http://www.grouplens.org/node/73
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Sun, J., Zhao, J., Yu, X. (2009). A Collaborative Filtering Algorithm with Phased Forecast. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_88
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DOI: https://doi.org/10.1007/978-3-642-02962-2_88
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