Abstract
With the development of information society, Recommendation System has been an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretable recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model based on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretability of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed based on the aspects and features of the products to be recommended, and users’ product satisfaction is predicted by combining sentiment tendency. Then the recommendation chain will be constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to be recommended to the target user, generate the recommendation results and personal explanation for the user by the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model based on the Fine-grained Data achieve better interpretability and performance of product recommendation systems.
This work was supported by the National Natural Science Foundation of China (61872161, 61602057), the Science and Technology Development Plan Project of Jilin Province (2018101328JC), the Science and Technology Department Excellent Youth Talent Foundation of Jilin Province (20170520059JH), the Project of Technical Tackle-Key-Problem of Jilin Province (20190302029GX), and the Project of Development and Reform of Jilin Province (2019C053-8).
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Ma, FY., Chen, WQ., Xiao, MH., Wang, X., Wang, Y. (2019). Explanation Chains Model Based on the Fine-Grained Data. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_63
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