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| Personalized Book Recommendation Based on User Preferences and Commodity Features |
Hou Yinxiu, Li Weiqing, Wang Weijun( ), Zhang Tingting |
School of Information Management, Central China Normal University, Wuhan 430079, China
Key Laboratory of Adolescent Cyber Psychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China |
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Abstract [Objective] This paper identifies the fine-grained preferences of online bookstore users, aiming to optimize the personalized book recommendation service. [Methods] First, we conducted sentiment analysis of the book features through readers’ comments, which indicated their preferences. Then, we calculated the books’ sentiment scores based on the readers’ comments. Finally, the user preferences matrix and the sentiment scores matrix were matched to personalize the book recommendation. [Results] We retrieved the needed data from Amazon’s book comments, and then conducted an experiment to compare the results of our new method with those of the traditional collaborative filtering methods. We found that the proposed method improved the precision, recall and coverage by 0.030, 0.097, 0.2812. [Limitations] We did not consider the impacts of time on user’s preferences, and the feature types might not be comprehensive due to the limited number and quality of Amazon’s book comments. [Conclusions] The proposed method improves the performance of personalized book recommendation service.
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Received: 22 May 2017
Published: 28 September 2017
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