将用户社会关系引入用户-商品评分数据中,构建用户-商品异构关系图,可缓解传统推荐系统面临的数据稀疏性和冷启动问题.但是,由于用户间社会关系的复杂性,聚合不一致的社会邻居可能会降低推荐性能.针对上述问题,文中提出基于自监督三重训练和聚合一致邻居的社会化推荐模型(Social Recommendation Based on Self-Supervised Tri-Training and Consistent Neighbor Aggregation,SR-STCNA).首先,在用户-商品评分数据的基础上,引入用户-用户间的社交关系,在用户-商品异构图中构建多种关系.使用超图表示用户和用户、用户和商品之间的关系.使用自监督三重训练,从未标记的数据中学习用户表示,充分挖掘用户-用户和用户-商品间存在的复杂连接关系.然后,通过用户-商品异构图上的节点一致性得分和关系自注意力,在用户和商品表示学习过程中聚合一致邻居,增强用户和商品嵌入表示能力,提高推荐性能.在CiaoDVD、FilmTrust、Last.fm、Yelp数据集上的实验表明,SR-STCNA性能较优.
Social Recommendation Model Based on Self-Supervised Tri-Training and Consistent Neighbor Aggregation
Integrating user social relationships into user-item rating data to construct a heterogeneous user-item graph can alleviate data sparsity and cold start in traditional recommender systems.However,due to the complexity of user social relationships,aggregating inconsistent neighbors may degrade the recommendation performance.To address this issue,a social recommendation model based on self-supervised tri-training and consistent neighbor aggregation(SR-STCNA)is proposed.Firstly,on the basis of user-item rating data,social relationships among users are introduced and diverse relations within the heterogeneous user-item graph are established.The relationships between users as well as between users and items are presented by a hypergraph.Self-supervised tri-training is employed to learn users'representations from unlabeled data and uncover the complex connectivity between user-user and user-item interactions.Then,the consistent neighbors of users and items are aggregated in the process of their representation learning by the node consistency score and relationship self-attention on the user-item heterogeneous graph.Consequently,the representation ability of users and items is enhanced,thereby improving the recommendation performance.Finally,the experimental results on CiaoDVD,FilmTrust,Last.fm and Yelp datasets validate the superiority of SR-STCNA.
Social RecommendationCollaborative FilteringData SparsityHypergraphConsistent Neighbor
NETL
NSTL
万方数据
