计算机科学与探索2025，Vol.19Issue(4)：1105-1114,10.DOI:10.3778/j.issn.1673-9418.2403047
社交知识感知网络推荐算法
Social Knowledge-Aware Network Recommendation Algorithm
摘要
Abstract
The recommendation algorithm can quickly discover items that users like and make effective recommenda-tions,thus greatly saving users'search time.However,although existing recommendation algorithms can make recom-mendations based on characteristics such as user preferences or item similarity,there are still problems such as cold starts of users and items,and data noise.In order to solve the above problems,a social knowledge-aware network recommenda-tion algorithm(SAGN)is proposed.This algorithm injects the knowledge of interdependence between items and users and the knowledge of correlation between users into the feature calculation of users and items.On the user side,this paper uses knowledge-aware networks to calculate the browsing records of users and their friends to obtain multiple preference features,combined with adaptive attention gating mechanism to generate user preference feature vectors;on the item side,this paper obtains a set of user friends associated with the item to be predicted,uses their browsing history as the initial en-tity set of the item,and uses the knowledge-aware network to extract item feature vectors based on the preferences of the user and his friends.In order to verify the effectiveness of the algorithm,comparative experiments are conducted on the real datasets Ciao and Epinions with algorithms such as SocialFD,GraphRec,SREPS,HGCL,and KR-GCN.Experimen-tal results show that compared with the best-performing model,the RMSE and MAE of the SAGN algorithm on the Epin-ions dataset are increased by 2.14%and 1.74%respectively;the RMSE and MAE on the Ciao dataset are increased by 1.81%and 1.79%respectively.关键词
推荐算法/社交网络/注意力机制/知识图谱Key words
recommendation algorithm/social network/attention mechanism/knowledge graph分类
计算机与自动化引用本文复制引用
金海波,冯雨静..社交知识感知网络推荐算法[J].计算机科学与探索,2025,19(4):1105-1114,10.基金项目
国家自然科学基金(62173171).This work was supported by the National Natural Science Foundation of China(62173171). （62173171）
