Research on personalised knowledge graph recommendation algorithm based on self-supervised learning

A recommendation algorithm that integrates generative graph self-supervised learning and contrastive graph self-supervised learning is proposed in this paper to address the issues of noisy data and graph incompleteness in knowledge graphs. Firstly, implicit semantics are captured by the self-supervised generative learning method through the masking and reconstruction of the edges of knowledge graph triples. Secondly, a multi-level contrastive learning method is proposed, which includes strategies for edge deletion and feature dropout in the graph structure and embedding layers, respectively.Finally, a contrastive loss function is introduced for contrastive learning to improve the quality of the embedded representations, and the BPR loss is incorporated and combined with the loss from the graph self-supervised learning module to optimize the overall performance of the recommender system. The results demonstrate that on the Last.fm, MovieLens, and Yelp2018 datasets, the model proposed in this paper significantly improves Recall and NDCG evaluation metrics compared to other traditional methods.