Abstract
The recommendation system achieves user preferences by analyzing users’ historical behaviors, find user groups similar to target users, predict target users’ ratings of items of interest and make recommendations. Therefore, various techniques have been proposed to develop similarity measures. Research on recommendation algorithms that integrate user trust information has made considerable progress. However, only the definition of trust relationship based on the degree of social relationship can’t truly reflect the trust relationship between users. Focusing on the shortcomings of current trust algorithms, this paper proposes an implicit trust calculation method that integrates user entropy. By using the information entropy of user ratings to improve the previous similarity measurement, the user’s trust degree is integrated to make more accurate selection of neighborhoods, and the situation of high trust and low interest similarity is reduced. The effectiveness of the algorithm is verified through experiments, it is proved that it is superior to the previous similarity measure, and compared with the traditional recommendation algorithm, the prediction accuracy has been improved.
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Acknowledgments
The paper is supported in part by the National Natural Science Foundation of China under Grant No. 61672022 and No. U1904186, Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University under Grant No. XXKZD1604.
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Cai, X., Tan, W., Zhang, X., Zhou, X. (2022). Using Entropy for Trust Measure in Collaborative Filtering. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_10
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DOI: https://doi.org/10.1007/978-3-031-23741-6_10
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