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- W2967492796 abstract "Social commerce is an important part of the social network which contains a large number of user behaviors and user relationships. Users generate reviews, social relations, user-product or product-product mapping information that can reflect an evolution of product characteristics and user preferences in using social commerce. It is a popular topic by using these information to conduct rating prediction in the field of intelligent recommendation. In this paper, optimizing the rating prediction based on topic analysis in two aspects. On the one hand, in the process of data preprocessing, constructing a dynamic hierarchical tree of topic words (DHTTW), which can not only capture the change of users’ preferences for product property, but also reflect the impact of different product property on users’ preferences at the same time. Based on DHTTW, designing the mapping rules from user reviews to DHTTW to generate user preference vectors. On the other hand, in the process of prediction, proposing a prediction method named combination of gradient boosting decision tree and multi-class linear regression (GBDT-MCLR), which further improves the accuracy of rating prediction." @default.
- W2967492796 created "2019-08-22" @default.
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- W2967492796 date "2019-01-01" @default.
- W2967492796 modified "2023-10-06" @default.
- W2967492796 title "Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis Approach" @default.
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- W2967492796 doi "https://doi.org/10.1007/978-3-030-30146-0_52" @default.
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