计算机科学与探索2025，Vol.19Issue(5)：1217-1229,13.DOI:10.3778/j.issn.1673-9418.2406021
融合自适应超图的自监督知识感知推荐模型
Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph
摘要
Abstract
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 sig-nals 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 em-beddings in the adaptive hypergraph.Secondly,it uses a relation-aware graph attention network to mine the rich knowl-edge 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 experimen-tally compared with benchmark models such as KGAT,KGIN,and KACL on several publicly available datasets.Com-pared 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.关键词
推荐系统/知识图谱/自适应超图/自监督学习/关系感知图注意网络Key words
recommendation system/knowledge graph/adaptive hypergraph/self-supervised learning/relation-aware graph attention network分类
计算机与自动化引用本文复制引用
周家旋,柳先辉,赵晓东,侯文龙,赵卫东..融合自适应超图的自监督知识感知推荐模型[J].计算机科学与探索,2025,19(5):1217-1229,13.基金项目
国家重点研发计划(2022YFB3305700). This work was supported by the National Key Research and Development Program of China(2022YFB3305700). （2022YFB3305700）
