JACIII Vol.14 No.6 pp. 654-660
doi: 10.20965/jaciii.2010.p0654


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

January 25, 2010
May 25, 2010
September 20, 2010
recommender system, collaborative filtering, similarity, Particle Swarm Optimization

The recommender system provides personalized recommendations at many e-commerce websites. Collaborative filtering is one of the most popular and effective recommendation algorithms. User-based collaborative filtering, the conventional approach in collaborative filtering, uses user similarity computed based on user item rating. Recommendations are provided by calculating rating predictions based on similarity. Pearson’s correlation coefficient or cosign distance is used as similarity. Until now, a lot of discussions for efficient similarity computation were given by many researchers. Despite active discussion of similarity computation, little computation has been made for optimal similarity in collaborative filtering. In this research, similarity optimization problem was formulated by defining similarities between an active user and other users as a vector variable. The quasi-optimal solution was obtained based on Particle Swarm Optimization (PSO) approach, compared to Pearson’s correlation coefficient. Experimental results based on agentbased simulation and sample dataset show that similarity based on PSO improves recommendation accuracy. We also found that PSO-based similarity computation provides rating predictions for unknown ratings more accurately than conventional similarity computation.

Cite this article as:
Akihiro Yamashita, Hidenori Kawamura, and Keiji Suzuki, “Similarity Computation Method for Collaborative Filtering Based on Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.6, pp. 654-660, 2010.
Data files:
  1. [1] P. Resnick and H. Varian, “Recommender Systems,” Comm. of the ACM, Vol.40, No.3, pp. 56-58, 1997.
  2. [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. [3] G. Linden, B. Smith, and J. York, “ Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Jan./Feb., 2003.
  4. [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. [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. [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. [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. [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. [9] E. Garcia, “Cosine Similarity and Term Weight Tutorial,” ,
  10. [10] S. Toby, “Programming Collective Intelligence: Building Smart Web 2.0 Applications,” O’reilly, 2007.
  11. [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. [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. [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. [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. [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. [16] J. Kennedy, “Swarm Intelligence,” Handbook of Nature-Inspired and Innobative Computing, pp. 187-219, 2006.
  17. [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. [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. [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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