Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph

To alleviate the cold-start problem that exists in traditional collaborative filtering recommendation systems, knowledge graphs have been introduced as a kind of auxiliary knowledge in recommendation systems. However, existing knowledge graph recommendation models have limitations in adequately modeling higher-order interactions, making it difficult to capture important information from higher-order neighbors. In addition, the sparsity problem of supervised signals also affects recommendation system performance. To address the above issues, a self-supervised knowledge-aware recommendation model integrating adaptive hypergraph is proposed. The model first utilizes a hybrid graph convolutional network to jointly learn the low-order interaction embeddings in the interaction graph and the higher-order interaction embeddings in the adaptive hypergraph. Secondly, it uses a relation-aware graph attention network to mine the rich knowledge information of users and items in the knowledge graph. Then, the model constructs a comparison learning task based on the three views, which mitigates the sparsity problem of the interaction data by introducing the self-supervised signals. Finally, the three kinds of embeddings are combined for subsequent recommendation prediction. The model is experimentally compared with benchmark models such as KGAT, KGIN, and KACL on several publicly available datasets. Compared with the best recommendation performance model among the seven compared models, on the MovieLens dataset, Recall@20 is improved by 1.22%, NDCG@20 is improved by 1.17%; on the Yelp2018 dataset, Recall@20 is improved by 1.41%, NDCG@20 is improved by 1.60%. Experimental results show that this model outperforms other benchmark models in terms of recommendation performance.