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Knowledge-Aware Multi-view Contrastive Learning for Recommendation
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DOI：10.1007/s11063-025-11750-0.png)
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Abstract
En 中文
Knowledge-aware Recommendation (KGR) aims to utilize a knowledge graph to provide rich side information for items in a recommendation system and construct a unified graph containing users, items, and entities. In this paper, we present a new graph neural network for user-item-entity interaction modeling, named Graph Attention Intent Network, it employs different strategies to aggregate user and item information to generate high-quality representations. Typically, the description of user-item interactions is modeled as a bipartite graph, which overlooks the relations between users and between items, a significant aspect of realistic recommendation. Therefore, we propose a framework, named knowledge-aware multi-view contrastive learning for recommendation. It can explore effective user-user and item-item relations in the heterogeneous network of KGR, construct a user social graph and an item similarity graph, and combine the information of the two views into user-item-entity interaction modeling to enhance the representation of users and items. We introduce cross-graph contrastive learning to facilitate the integration of heterogeneous information while alleviating the sparse labeling problem of recommendation tasks. Experimental results on three benchmark datasets show that our model is more effective than other state-of-the-art models.
Keywords:
Recommendation
Knowledge graph
Graph neural network
Multi-view representation learning
Constrastive learning
Journal
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2.8
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175
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5.5K
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Cited Papers
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Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
OAAI
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
OAAIShareSaveCollaborative Knowledge Base Embedding for Recommender SystemsProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
PREAI
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