Abstract
The Collaborative filtering algorithm predicts the user’s preference for the project to complete a recommendation by analyzing the user preference data, and usually takes the user’s rating as the user preference data. However, there is a bias between user’s preference and user’s score of the real scene, so the user’s rating as user preference can lead to lower recommendation accuracy. For this problem, this paper proposes a user preference extraction method based on attribution theory, calculates user preferences by analyzing user rating behavior. Then, combining preference similarity and rate similarity, making up the bias between user rating and user preference in collaborative filtering algorithm. Experimental verification on universal Dataset Movies lens-1m results shows that the algorithm is preferable to the existing collaborative filtering algorithm.
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DeLei, M., Yan, T., Bing, L. (2018). A Collaborative Filtering Algorithm Based on Attribution Theory. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_28
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DOI: https://doi.org/10.1007/978-3-319-93818-9_28
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