山西大学学报（自然科学版）2024，Vol.47Issue(2)：269-278,10.DOI:10.13451/j.sxu.ns.2023167
项目邻居信息对比增强的推荐方法
Recommendation Method for Contrastive Enhancement of Item Neighborhood Information
摘要
Abstract
Aiming at the weak user-item interaction supervision signal in the knowledge graph-based recommendation method and the problem that the knowledge graph contains noise information,in this paper,we propose a recommendation method for contras-tive enhancement of item neighbor information(RMCEIN).The RMCEIN obtains the multi-order neighbor embedding of users and items through heterogeneous propagation and knowledge-aware attention function,which is used to enrich the characteristics of us-ers and items;in the process of item neighbor embedding,it adopts the method of adding uniformly distributed weak noise to con-struct item neighbor enhancement view,which can effectively reduce the time overhead of view construction.In addition,through contrastive learning between two item neighbor views,the contrastive loss function is called to promote the uniformity of item view information,adjust the neighbor structure of items,achieve the purpose of reducing knowledge noise in the knowledge graph,and at the same time introduce multi-task learning to alleviate the supervision signal weak problem.In order to verify the effectiveness of the method,experiments were carried out on the MovieLens-1M,Book-Crossing and Last-FM datasets,and the experimental results were compared with 10 methods such as RippleNet(Propagating User Preferences on the Knewledge Graph for Recommender Sys-tems),CKAN,KGIC,etc.The AUC(Area Under Curve)of the method in this paper increased by 2.32％on average.F1 value in-creased by 2.26 on average.关键词
知识降噪/多任务学习/知识图谱/推荐方法Key words
knowledge distillation/multi-task learning/knowledge graph/recommended method分类
信息技术与安全科学引用本文复制引用
周北京,王海荣,马赫,张丽丝..项目邻居信息对比增强的推荐方法[J].山西大学学报（自然科学版）,2024,47(2):269-278,10.基金项目
宁夏自然科学基金(2023AAC03316) （2023AAC03316）
北方民族大学研究生创新项目(YCX23146 （）
YCX23159) （）
