Abstract
Recommendation is to offer information which fits user’s interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. Collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users’ preferences for the target item or the target user’s preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.
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References
Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13, 393–408 (1999)
Cheung, K.W., Kwok, J.T., Law, M.H., Tsui, K.C.: Mining Customer Product Ratings for Personalized Marketing. Decision Support Systems 35, 231–243 (2003)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Inc., Englewood Cliffs (1999)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., And Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40, 77–87 (1997)
Sarwar, B.M., Karypis, G., Konstan, J.A., Ried, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proceedings of the ACM EC 2000 Conference, Minneapolis, MN, pp. 158–167 (2000)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++, 2nd edn. Cambridge University Press, Cambridge (2002)
McJones, P.: Eachmovie Collaborative Filtering Data Set. DEC Systems Research Center (1997), http://www.rearchdigital.com/SRC/eachmovie
Kim, M.W., Kim, E.J., Ryu, J.W.: A Collaborative Recommendation Based on Neural Networks. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 425–430. Springer, Heidelberg (2004)
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Kim, E., Kim, M., Ryu, J. (2005). Collaborative Filtering Based on Neural Networks Using Similarity. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_57
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DOI: https://doi.org/10.1007/11427469_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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