Paper:
Similarity Computation Method for Collaborative Filtering Based on Optimization
Akihiro Yamashita, Hidenori Kawamura, and Keiji Suzuki
Graduate School of Information Science and Technology, Hokkaido University, Kita14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
- [1] P. Resnick and H. Varian, “Recommender Systems,” Comm. of the ACM, Vol.40, No.3, pp. 56-58, 1997.
- [2] J. B. Schafer, J. A. Konstan, and J. Riedl, “E-Commerce recommendation applications,” Data Mining and Knowledge Discovery, Vol.5, Nos.1-2, pp. 115-153, 2001.
- [3] G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Jan./Feb., 2003.
- [4] D. Goldberg, D. Nichols, B. M. Oki, and D. B. Terry, “Using Collaborative Filtering to Weave an Information Tapestry,” Communications of the ACM, Vol.35, No.12, pp. 61-70, 1992.
- [5] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.
- [6] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating Collaborative Filtering Rrecommender Systems,” ACM Trans. on Information Systems, Vol.22, No.1, pp. 5-53, 2004.
- [7] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An algorithmic framework for performing collaborative filtering,” Proc. of the 22nd annual Int. ACM SIGIR Conf. on Research and development in information retrieval, August, pp. 230-237, 1999.
- [8] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. on Knowledge and Data Engineering, Vol.17, No.6, pp. 734-749, 2005.
- [9] E. Garcia, “Cosine Similarity and Term Weight Tutorial,”
http://www.miislita.com/information-retrieval-tutorial/cosinesimilarity-tutorial.html#Cosim ,
2006. - [10] S. Toby, “Programming Collective Intelligence: Building Smart Web 2.0 Applications,” O’reilly, 2007.
- [11] H. Kwon, T. Lee, and K. Hong, “Using Entropy-based Similarity Measures Using Entropy-based Similarity Measures,” Proc. of the 2009 Int. Symp. on Web Information Systems and Applications (WISA’09), pp. 029-034, 2009.
- [12] J. L. Herlocker, J. A. Konstan, and J. Riedl, “An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms,” Information Retrieval, Vol.5, No.4, pp. 287-310, 2002.
- [13] H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” In Proc. of 30th Annual Int. ACM SIGIR Conf. on Information Retrieval, pp. 39-46, 2007.
- [14] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. IEEE Int. Conf. on Neural Networks, IEEE Service Center, Piscataway, NJ, IV, pp. 1942-1948, 1995.
- [15] J. Kennedy, “The particle swarm: social adaptation of knowledge,” Proc. IEEE Int. Conf. on Evolytionary Computation, IEEE Service Center, Piscataway, NJ, pp. 303-308, 1997.
- [16] J. Kennedy, “Swarm Intelligence,” Handbook of Nature-Inspired and Innobative Computing, pp. 187-219, 2006.
- [17] Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” Evolutionary Programming VII, Lecture Notes in Computer Science, Vol.1447, pp. 591-600, 1998.
- [18] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” Proc. of the 1999 Congress of Evolutionary Computation, IEEE Press, Vol.3, pp. 1945-1950, 1999.
- [19] B. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Item-based Collaborative Filtering Recommendation Algorithms,” Proc. of WWW’01, pp. 285-295, 2001.
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