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.
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References
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of UAI (1998)
Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proc. of SIGIR (1999)
Claypool, M., Le, M.W.P., Brown, D.C.: Implicit interest indicators. In: Proc. of IUI (2001)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)
Eyheramendy, S., Lewis, D., Madigan, D.: On the naive bayes model for text categorization. In: Proc. of Artificial Intelligence and Statistics (2003)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proc. of SIGIR (1999)
Hiemstra, D.: Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term. In: Proc. of SIGIR (2002)
Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. In: Proc. of IJCAI (1999)
Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proc. of SIGIR (1993)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: Proc. of CIKM (2001)
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)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 76–80 (Januvary/Febuary 2003)
Marlin, B.: Collaborative filtering: a machine learning perspective. Master’s thesis, Department of Computer Science, University of Toronto (2004)
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)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proc. of SIGIR (1998)
Salton, G., McGill, M.J.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proc. of the WWW Conference (2001)
van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
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)
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)
Zhai, C., Lafferty, J.D.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proc. of SIGIR (2001)
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, J., de Vries, A.P., Reinders, M.J.T. (2006). A User-Item Relevance Model for Log-Based Collaborative Filtering. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_5
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DOI: https://doi.org/10.1007/11735106_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33347-0
Online ISBN: 978-3-540-33348-7
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