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Collaborative Filtering for Recommendation based on Neural Network  

김은주 (숭실대학원 컴퓨터학과)
류정우 (숭실대학원 컴퓨터학)
김명원 (숭실대학원 컴퓨터학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.4, 2004 , pp. 457-466 More about this Journal
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. The 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.
Keywords
Recommendation; Neural Network; Collaborative Filtering; Data Fusion; Nearest Neighbor Method;
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Times Cited By KSCI : 1  (Citation Analysis)
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