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Knowledge-Flow Contrastive Learning for recommendation
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DOI：10.1016/j.inffus.2025.103477.png)
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Abstract
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• KFCL can mitigate the noise introduced by CL in a KG-aware recommender system. • User preference is enhanced by knowledge fusion and multi-views feature fusion. • The complementary roles of contrastive learning and feature fusion are explored. • Analyzed and demonstrated the source of noise in the KG-aware recommender system.
Keywords:
Knowledge graph
Contrastive learning
Feature fusion
Graph neural networks
Recommender system
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15.5
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Cited Papers
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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
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