Knowledge-Variational Contrastive Learning for Recommendation

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 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 contain lots of task-irrelevant entities, and the user-item interactions constructed form implicit feedback is usually noisy. The noisy data will generate intrusive supervised and self-supervised signal and will degrade recommendation performance. To address these limitations,we propose a novel KG-enhanced self-supervised learning recommendation method, named K nowledge - V ariational C ontrastive L earning for Recommendation (KVCL). Specifically, we first design an adaptively denoising mechanism to identify and prune the noisy data in 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.