Machine Learning

, Volume 72, Issue 3, pp 231–245 | Cite as

A collaborative filtering framework based on both local user similarity and global user similarity

  • Heng Luo
  • Changyong Niu
  • Ruimin Shen
  • Carsten Ullrich
Article

Abstract

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.

Keywords

Collaborative filtering Similarity measure Information theory 

References

  1. Aho, A. V., & Hopcroft, J. E. (1974). The design and analysis of computer algorithms. Boston: Addison-Wesley. MATHGoogle Scholar
  2. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the fourteenth annual conference on uncertainty in artificial intelligence (pp. 3–52). Google Scholar
  3. Cormen, T. H., Leiserson, C. E., & Rivest, R. L. (1992). Introduction to algorithms. Cambridge: MIT Press. Google Scholar
  4. DeCoste, D. (2006). Collaborative prediction using ensembles of maximum margin matrix factorizations. In Proceedings of the 23rd international conference on machine learning (pp. 249–256). Google Scholar
  5. Fouss, F., Pirotte, A., Renders, J. M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 355–369. Google Scholar
  6. Gori, M., & Pucci, A. (2007). ItemRank: a random-walk based scoring algorithm for recommender engines. In IJCAI (pp. 2766–2771). Google Scholar
  7. Herlocker, J. L., Konstan, J. A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (pp. 230–237). Google Scholar
  8. Hofmann, T., & Puzicha, J. (1999). Latent class models for collaborative filtering. In Proceedings of the sixteenth international joint conference on artificial intelligence (pp. 688–693). Google Scholar
  9. Jin, R., Chai, J. Y., & Si, L. (2004). An automatic weighting scheme for collaborative filtering. In Proceedings of the 27th annual international conference on research and development in information retrieval (pp. 337–344). Google Scholar
  10. Kim, K. H., & Choi, S. (2007). Neighbor search with global geometry: a minimax message passing algorithm. In Proceedings of the 24th international conference on machine learning (pp. 401–408). Google Scholar
  11. Kotz, S., Kozubowski, T. J., & Podgrski, K. (2001). The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance. Basel: Birkhauser. MATHGoogle Scholar
  12. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: item-to-item collaborative filtering. IEEE Transactions on Internet Computing, 7(1), 76–80. CrossRefGoogle Scholar
  13. Ma, H., King, I., & Lyu, M. R. (2007). Effective missing data prediction for collaborative filtering. In SIGIR ’07: proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (pp. 39–46). New York: ACM. Google Scholar
  14. Marlin, B. (2004a). Modeling user rating profiles for collaborative filtering. Advances in Neural Information Processing Systems, 16, 627–634. Google Scholar
  15. Marlin, B. (2004b). Collaborative filtering: a machine learning perspective. Master thesis, University of Toronto. Google Scholar
  16. Norton, R. M. (1984). The double exponential distribution: Using calculus to find a maximum likelihood estimator. The American Statistician, 38(2), 135–136. CrossRefGoogle Scholar
  17. Pennock, D. M., Horvitz, E., Lawrence, S., & Giles, C. L. (2000). Collaborative filtering by personality diagnosis: a hybrid memory-and model-based approach. In Proceedings of the 16th conference on uncertainty in artificial intelligence (pp. 473–480). Google Scholar
  18. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on computer supported cooperative work (pp. 175–186). Google Scholar
  19. Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285–295). Google Scholar
  20. Shardanand, U., & Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 210–217). Google Scholar
  21. Soboroff, I., & Nicholas, C. (2000). Collaborative filtering and the generalized vector space model (poster session). In Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval (pp. 351–353). Google Scholar
  22. Wang, J., de Vries, A. P., & Reinders, M. J. T. (2006). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 501–508). Google Scholar
  23. Xue, G. R., Lin, C., Yang, Q., Xi, W. S., Zeng, H. J., Yu, Y., et al. (2005). Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval (pp. 114–121). Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Heng Luo
    • 1
  • Changyong Niu
    • 1
  • Ruimin Shen
    • 1
  • Carsten Ullrich
    • 1
  1. 1.Department of Computer Science and TechnologyShanghai Jiao Tong UniversityShanghaiChina

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