Multi-perspective social recommendation method with graph representation learning

Social recommender systems (SRS) aim to study how social relations influence users' choices and how to use them for better learning users embeddings. However, the diversity of social relationships, which is instructive to the propagation of social influence, has been rarely explored. In this paper, we propose a graph convolutional network based representation learning method, namely multi-perspective social recommendation (MPSR), to construct hierarchical user preferences and assign friends' influences with different levels of trust from varying perspectives. We further utilize the attributes of items to partition and excavate users' explicit preferences and employ complementary perspective modeling to learn implicit preferences of users. To measure the trust degree of friends from different perspectives, the statistical information of users' historical behavior is utilized to construct multi-perspective social networks. Experimental results on two public datasets of Yelp and Ciao demonstrate that the MPSR significantly outperforms the state-of-the-art methods. Further detailed analysis verifies the importance of mining explicit characteristics of users and the necessity for diverse social relationships, which show the rationality and effectiveness of the proposed model. The source Python code will be available upon request. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a multi-perspective social recommendation method based on graph convolutional networks to construct user preferences and evaluate friends' influences from different angles. Experimental results show that the proposed method significantly outperforms state-of-the-art methods on two public datasets. Mining explicit user features and diverse social relationships are crucial for the accuracy and effectiveness of the model.