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.
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Acknowledgments
This work is supported by National Natural Science Foundation (61662013, U1501252, U1711263, U1811264,61662015, 61562014); Guangxi Innovation-Driven Development Project (Science and Technology Major Project)(AA17202024); The Guangxi Natural Science Foundation (2017GXNSFAA198372, 2016GXNSFAA380149); The Funds of Guangxi Key Lab of Trusted software Project (kx201511); The Teacher Growth Fund of the Education Development Foundation of Guangxi Normal University (EDF2015005); The Funds of Graduate student innovation program Guilin University of Electronic Technology (2017YJCX56, 2019YCXS045).
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Zhang, H., Zhong, H., Yang, Q., Jia, F., Zhou, Y., Pan, F. (2019). Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis Approach. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_52
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