Knowledge Fusion and Denoising with Cross-View Contrastive Learning: A Framework for Efficient Sparse Data Processing

Recommender systems are effective tools to alleviate the challenges posed by information overload, but data sparsity has greatly affected their performance. Knowledge graphs (KG) can be employed to solve this problem because of their rich side information. However, existing KG-enhanced recommendation methods have the following limitations: (1) Noisy interactions: the user-item interactions constructed from implicit feedback are noisy, which is harmful to the user’s true preferences learning; (2) Insufficient fusion between different views: most KG-enhanced recommendation methods ignore the differences between signals from KG and user-item interactions and fail to fuse them effectively. To address these limitations, we propose a novel KG-enhanced recommendation framework named K nowledge F usion and D enoising with cross-view C ontrastive L earning framework (KFDCL). Specifically, we first perform contrastive learning (CL) across KG (knowledge view) and user-item interaction data (collaborative view) and then explicitly prune noisy interactions to alleviate the noisy interactions problem. Additionally, we design a weighted fusion strategy based on the activity of users and items to fuse the signals from different views adaptively. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed KFDCL, notably with an average improvement of 3.79% performance over state-of-the-art methods on Recall@20 and NDCG@20.