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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Kim, M.W., Kim, E.J., Ryu, J.W. (2005). Collaborative Filtering for Recommendation Using Neural Networks. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424758_14
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DOI: https://doi.org/10.1007/11424758_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25860-5
Online ISBN: 978-3-540-32043-2
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