Research Article
Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis Approach
@INPROCEEDINGS{10.1007/978-3-030-30146-0_52, author={Huibing Zhang and Hao Zhong and Qing Yang and Fei Jia and Ya Zhou and Fang Pan}, title={Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis Approach}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={8}, keywords={Social commerce Reviews Rating prediction Dynamic Hierarchical Tree of Topic Words Multi-Class Linear Regression}, doi={10.1007/978-3-030-30146-0_52} }- Huibing Zhang
Hao Zhong
Qing Yang
Fei Jia
Ya Zhou
Fang Pan
Year: 2019
Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis Approach
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-30146-0_52
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.