基于高阶和时序特征的图神经网络社会化推荐算法研究

doi:10.11896/jsjkx.220700108

Abstract

Abstract: Cross-item social recommendation is a method of integrating social relationships into the recommendation system.In social recommendation,user is the bridge connecting user-item interaction graph and user-user social graph.So user representation learning is essential to improve the performance of social recommendation.However,existing methods mainly use static attributes of users or items and explicit friend information in social networks for representations learning,and the temporal information of the interaction between users and items and their implicit friend information are not fully utilized.Therefore,in social recommendation,effective use of temporal information and social information has become one of the important research topics.This paper focuses on the temporal information of the interaction between users and items,and gives full play to the advantages of social network,modeling the user's implicit friends and item's social attributes.This paper proposes a novel graph neural networks social recommendation based on high-order and temporal features,referred to as HTGSR.Firstly,the framework uses gated recurrent unit to model item-based user representations to reflect the user's recent preferences,and defines a high-order mo-deling unit to extract the user's high-order connected features and obtain the user's implicit friend information.Secondly,HTGSR uses attention mechanism to obtain social-based user representation.Thirdly,the paper proposes different ways to construct item's social networks,and uses the attention mechanism to obtain item representations.Finally,the user's and item's representations are input to the MLP to complete the user's rating prediction for the item.The paper conducts specific experiments on two public and real-world datasets,and compares the experimental results with different recommendation algorithms.The results show that the HTGSR has achieved good results on the two datasets.

Key words: Social recommendation, Temporal features, Graph neural networks, High-order features

CLC Number:

TP311

YU Jian, ZHAO Mankun, GAO Jie, WANG Congyuan, LI Yarong, ZHANG Wenbin. Study on Graph Neural Networks Social Recommendation Based on High-order and Temporal Features[J].Computer Science, 2023, 50(3): 49-64.

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