Abstract
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 selection of 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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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, M.W., Kim, E.J., Ryu, J.W. (2004). A Collaborative Recommendation Based on Neural Networks. In: Lee, Y., Li, J., Whang, KY., Lee, D. (eds) Database Systems for Advanced Applications. DASFAA 2004. Lecture Notes in Computer Science, vol 2973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24571-1_39
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DOI: https://doi.org/10.1007/978-3-540-24571-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21047-4
Online ISBN: 978-3-540-24571-1
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