Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce |
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Authors: | Chun-Hui Piao Jing Zhao Li-Juan Zheng |
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Affiliation: | (1) School of Information, Renmin University of China, 100872 Beijing, China;(2) School of Economics and Management, Shijiazhuang Railway Institute, 050043 Shijiazhuang, China;(3) School of Computer and Information Engineering, Shijiazhuang Railway Institute, 050043 Shijiazhuang, China |
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Abstract: | Based on the introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aiming at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users’ hidden interests. Using the data selected from Movielens and Book-Crossing datasets and MAE accuracy metric, three different collaborative filtering recommendation algorithms are compared through experiments. The experimental scheme and results are discussed in detail. The results show that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation accuracy comparable to the item-based algorithm. At last, a solution to B2B e-commerce recommendation applications based on Web services technology is proposed, which adopts entropy-based collaborative filtering recommendation algorithm. |
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Keywords: | Personalized recommendation Entropy Collaborative filtering algorithm Similarity measure B2B e-commerce Web services MAE |
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