关键词: 推荐系统, 知识图谱, 对比学习, 图神经网络, 自监督学习

Abstract: A knowledge-aware recommendation model, called Fusion of Denoising Strategies and Multi-View Contras-tive Learning (FDSMVC), is proposed to address the issues of poor noise reduction effect, inadequate extrac-tion of semantic information between items, and imbalanced utilization of heterogeneous information in knowledge graph-based recommendation algorithms. Firstly, noise reduction is performed on the user-item interaction graph and the knowledge graph through selectively dropping edges and masking low-weight triplets using a weighted function Secondly, random singular value decomposition, cosine similarity, k-nearest neighbors (kNN) sparsity, and path-based graph attention networks are used to construct collaborative view, semantic view, and structural view of item-item relationships. Next, contrastive learning is applied to learn representations within each view, considering intra-graph, local, and global contexts. Finally, a multi-task optimization framework is applied to jointly optimize the recommendation task and the contrastive learning task, resulting in the prediction of user-item interactions. Experimental evaluations on five publicly available datasets demonstrate that the proposed FDSMVC model outperforms the state-of-the-art baseline methods, with improvements of 1.06%-2.04% in terms of AUC, 1.52%-2.60% in terms of F1 score, and better recall@K performance compared to the best baseline method.

Key words: Recommendation system, Knowledge graph, Contrastive learning, Graph neural network, Self-supervised learning