[Objective] This paper conducts group recommendation using the relationship among users, tags and books.[Methods] First, we used the K-means algorithm to cluster users and books. Then, we calculated cosine similarity of the two groups. Third, we compared various books based on their reviews. Finally, we sorted and clustered books to personalize the recommendation results.[Results] We examined the proposed model with data from “Douban Net” and our model recommended better resources for user groups.[Limitations] The sample data size needs to be expanded.[Conclusions] The proposed model improves the personalized recommendation of books.
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Xiong Huixiang,Li Xiaomin,Li Yueyan. Group Recommendation Based on Attribute Mining of Book Reviews. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 214-222.
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