社交电商中融合信任和声誉的图神经网络推荐研究

doi:10.16381/j.cnki.issn1003-207x.2021.0708

Abstract

Abstract: Amid rapid development of social network, growing number of users leads to excessive information, challenging the beneficiaries in theory to filter the valuable bits. One question raised along this, which requires users to identify trustworthy content, presents researchers with the idea of studying the role of which trust relations among users play in social e-commerce. Some exploring in this field sees a connection between item ratings and trust relations of users, however, there often lacks integrity in reliable trust relation data, and even those that doesn’t show great disparity. Hence, a method to identify content and information that users trusted more swiftly and precisely is still in great demand. Also, researchers show increasing interest in the position that user reputation played in effecting user trust and how to merge those two to offer more satisfying recommendation.

Key words: trust relationship, social reputation, graph neural network, social commerce, recommendation system

CLC Number:

F224

HU Chunhua, DENG Ao, TONG Xiaoqin, MIAO He, WANG Zongrun. A Graph Neural Network Recommendation Study Combining Trust and Reputation in Social E-commerce[J]. Chinese Journal of Management Science, 2021, 29(10): 202-212.

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A Graph Neural Network Recommendation Study Combining Trust and Reputation in Social E-commerce

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