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A User-Item Relevance Model for Log-Based Collaborative Filtering

  • Jun Wang
  • Arjen P. de Vries
  • Marcel J. T. Reinders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)

Abstract

Implicit acquisition of user preferences makes log-based collaborative filtering favorable in practice to accomplish recommendations. In this paper, we follow a formal approach in text retrieval to re-formulate the problem. Based on the classic probability ranking principle, we propose a probabilistic user-item relevance model. Under this formal model, we show that user-based and item-based approaches are only two different factorizations with different independence assumptions. Moreover, we show that smoothing is an important aspect to estimate the parameters of the models due to data sparsity. By adding linear interpolation smoothing, the proposed model gives a probabilistic justification of using TF×IDF-like item ranking in collaborative filtering. Besides giving the insight understanding of the problem of collaborative filtering, we also show experiments in which the proposed method provides a better recommendation performance on a music play-list data set.

Keywords

Information Retrieval Background Model Target Item Text Retrieval Language Modeling Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of UAI (1998)Google Scholar
  2. 2.
    Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proc. of SIGIR (1999)Google Scholar
  3. 3.
    Claypool, M., Le, M.W.P., Brown, D.C.: Implicit interest indicators. In: Proc. of IUI (2001)Google Scholar
  4. 4.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRefGoogle Scholar
  5. 5.
    Eyheramendy, S., Lewis, D., Madigan, D.: On the naive bayes model for text categorization. In: Proc. of Artificial Intelligence and Statistics (2003)Google Scholar
  6. 6.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proc. of SIGIR (1999)Google Scholar
  7. 7.
    Hiemstra, D.: Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term. In: Proc. of SIGIR (2002)Google Scholar
  8. 8.
    Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. In: Proc. of IJCAI (1999)Google Scholar
  9. 9.
    Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proc. of SIGIR (1993)Google Scholar
  10. 10.
    Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: Proc. of CIKM (2001)Google Scholar
  11. 11.
    Lafferty, J., Zhai, C.: Probabilistic relevance models based on document and query generation. In: Language Modeling and Information Retrieval. Kluwer International Series on Information Retrieval, vol. 13 (2003)Google Scholar
  12. 12.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 76–80 (Januvary/Febuary 2003)Google Scholar
  13. 13.
    Marlin, B.: Collaborative filtering: a machine learning perspective. Master’s thesis, Department of Computer Science, University of Toronto (2004)Google Scholar
  14. 14.
    Pennock, D.M., Horvitz, E., Lawrence, S., Giles, C.: Collaborative filtering by personality diagnosis: a hybrid memory and model based approach. In: Proc. of UAI (2000)Google Scholar
  15. 15.
    Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proc. of SIGIR (1998)Google Scholar
  16. 16.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)MATHGoogle Scholar
  17. 17.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proc. of the WWW Conference (2001)Google Scholar
  18. 18.
    van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)MATHGoogle Scholar
  19. 19.
    Wang, J., Pouwelse, J., Lagendijk, R., Reinders, M.R.J.: Distributed collaborative filtering for peer-to-peer file sharing systems. In: Proc. of the 21st Annual ACM Symposium on Applied Computing (2006)Google Scholar
  20. 20.
    Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: Proc. of SIGIR (2005)Google Scholar
  21. 21.
    Zhai, C., Lafferty, J.D.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proc. of SIGIR (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Wang
    • 1
  • Arjen P. de Vries
    • 1
    • 2
  • Marcel J. T. Reinders
    • 1
  1. 1.Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
  2. 2.CWIAmsterdamThe Netherlands

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