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A motif based hypergraph multi-level semantic encoding framework for social recommender systems
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AI
DOI：10.1016/j.sigpro.2024.109797.png)
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Abstract
En 中文
Introducing social relations to improve recommendation quality, namely social recommender systems (SocialRS), has become a hot topic in academic and industrial communities. While many promising models have been proposed, most of them focus on mining latent user preferences from pairwise relations. However, in many real-world applications, user preferences are always implicit in both low-order relations and high-order interactive patterns. To address this issue, we develop a novel SocialRS framework, called Hypergraph Multilevel Semantic Encoding SocialRS ( HMSE-SR ). Specifically, the model first constructs multi-view hypergraphs conditioned on different types of motifs. On this basis, we propose a multi-level embedding learning paradigm that integrates the local interactive relation encoder with global hypergraph structure learning, so as to comprehensively mine latent user preferences from both low-level and high-level semantic levels. Furthermore, to overcome the problem caused by scarcity and skewed distribution of user relations in reality, we enhance the hypergraph encoder via distilling self-supervision signals across the local and global structure levels. Finally, a joint optimization model is developed to train the HMSE-SR. To verify the superiority of the proposed model, extensive experiments are conducted on four real-world datasets under both general and cold-start settings.
Keywords:
Graph neural networks
Social recommender systems
Multi-level structure learning
Motif
Hypergraph contrastive learning
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