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Knowledge-Variational Contrastive Learning for Recommendation

Year: 2026
Type: article
Abstract: Recommender systems are effective tools to alleviate the challenges posed by information overload, but data sparsity has greatly affected their performance. Knowledge Graphs (KGs) and self-supervised learning are used to alleviate the data sparsity problem. However, existing KG-enhanced self-supervised learning recommendation methods have the following limitations: (1) Generality : existing CL-based recommendation methods strongly rely on manually designed data augmentation strategies, leading to poor generality of the CL-based models. (2) Robustness : KG usually contains lots of task-irrelevant entities, and the user–item interactions constructed from implicit feedback are usually noisy. The noisy data will generate intrusive supervised and self-supervised signals and will degrade recommendation performance. To address these limitations, we propose a novel KG-enhanced self-supervised learning recommendation method, named Knowledge-Variational Contrastive Learning for Recommendation (KVCL) . Specifically, we first design an adaptively denoising mechanism to identify and prune the noisy data in the KG and user–item interaction bipartite graph. Then, we learn a normal distribution for each node by the variational auto-encoder, and sample multiple times from the learned distribution to obtain different contrastive views. Extensive experiments based on three public datasets show that KVCL achieves improved performance over state-of-the-art methods, notably with 3.13% performance gain over state-of-the-art methods on Recall@20 and NDCG@20. Furthermore, evaluations including ablation studies and detailed analyses of multi-scenarios, computational efficiency, complexity, and denoising interpretability further underscore its scalability and practical applicability.
DOI: 10.1145/3805046
Language: English
Open Access status: hybrid
OpenAlex ID: W7147064444
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Cites: 38
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