Reader Preference Analysis and Book Recommendation Model with Attention Mechanism of Catalogs
Wang Dailin1,Liu Lina2(),Liu Meiling2,Liu Yaqiu2
1Northeast Forestry University Library, Harbin 150040, China 2College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
[Objective] This paper proposes a new reader preference analysis method as well as a personalized book recommendation model (IABiLSTM), aiming to improve the accuracy of the existing algorithms. [Methods] First, we extracted the semantic features of books according to their titles and catalog contents. We used the BiLSTM network to capture the long-distance dependency of the texts and word order context information. We also utilized the Two-layer Self-Attention mechanism to enhance the deeper semantic expression of book catalog features. Then, we analyzed readers’ historical browsing behaviors, which were quantified with interest function. Third, we combined the semantic features of books with readers’ interests to generate their preference vector. Fourth, we calculated the similarity between the vectors of candidate books’ semantic features and readers’ preferences, and predicted the scores for personalized book recommendation. [Results] We examined our model on Douban Reading and Amazon datasets, and set the N value as 50. The MSE,Precision and Recall reached 1.1%, 89.1%, and 85.2%, on the Douban data, while they were 1.2%, 75.2%, and 72.8% with the Amazon data. These performance were better than those of the comparison model. [Limitations] More research is needed to examine our model with other datasets. [Conclusions] The proposed model improves the accuracy of book recommendation, and benefits common NLP tasks.
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Wang Dailin, Liu Lina, Liu Meiling, Liu Yaqiu. Reader Preference Analysis and Book Recommendation Model with Attention Mechanism of Catalogs. Data Analysis and Knowledge Discovery, 2022, 6(9): 138-152.
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