Sociological-Theory-Based Multitopic Self-Supervised Recommendation

Qin Zhao

doi:10.1109/TNNLS.2024.3477720

Sociological-Theory-Based Multitopic Self-Supervised Recommendation

2024-01-01

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AI

DOI：10.1109/TNNLS.2024.3477720

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Social relationships offer crucial supplementary information for recommendations by leveraging users' social connections to gain insights into their preferences. However, prevalent social recommendation methods often grapple with the issues of sparsity and noise, which curtail their effectiveness. In addition, these methods overlook the intricacies of user interactions within social networks, which could provide invaluable information. Addressing their deficiencies, this article introduces a novel sociological-theory-based multitopic self-supervised recommendation method (SMSR). This method integrates user attitude information into the construction of social relationships and utilizes dynamic routing to identify and categorize topics, thereby mitigating the impact of social noise on recommendation accuracy. Furthermore, we reveal sophisticated higher order user relations within these topics by using motifs. By combining the light graph convolutional network with balance theory, SMSR efficiently aggregates information from diverse social relations to gain its outstanding performance. Moreover, we have devised and integrated four self-supervised signals, inspired by social theory and derived from heterogeneous graph analysis, to more effectively exploit the rich structural and semantic information inherent in social relationship graphs. Empirical results from extensive experiments on publicly available datasets underscore SMSR's superiority over the state of the art.

Keyword:

Social networking (online)

Aggregates

Urban areas

Recommender systems

Peer-to-peer computing

Contrastive learning

Semantics

Data mining

Convolution

Accuracy

Balance theory

graph neural network (GNN)

multitopic analysis

self-supervised learning

signed network

social recommendation

status theory

Sociological-Theory-Based Multitopic Self-Supervised Recommendation

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