Social Knowledge-Aware Network Recommendation Algorithm

doi:10.3778/j.issn.1673-9418.2403047

Abstract

Abstract: The recommendation algorithm can quickly discover items that users like and make effective recommendations, thus greatly saving users’ search time. However, although existing recommendation algorithms can make recommendations 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 recommendation 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 entity 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. Experimental results show that compared with the best-performing model, the RMSE and MAE of the SAGN algorithm on the Epinions 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.

Key words: recommendation algorithm, social network, attention mechanism, knowledge graph

摘要： 推荐算法能够快速挖掘用户喜好的物品并进行有效推荐，从而极大地节省用户搜索的时间。然而，现有推荐算法虽然可以根据用户的喜好或物品相似度等特点进行推荐，但仍然存在用户和物品的冷启动、数据噪声等问题。为解决上述问题，提出了一种社交知识感知网络推荐算法（SAGN），该算法将物品-用户之间相互依赖的知识以及用户之间相关联的知识注入到用户与物品的特征计算中。在用户端，利用知识感知网络计算用户及其朋友的浏览记录以获取多种偏好特征，并结合自适应注意力门控机制生成用户偏好特征向量；在物品端，获取与待预测物品建立关联的用户朋友集合，将朋友的浏览记录作为物品的初始实体集，使用知识感知网络提取基于用户及其朋友偏好的物品特征向量。为验证算法的有效性，在真实数据集Ciao和Epinions上与SocialFD、GraphRec、SREPS、HGCL、KR-GCN等算法进行对比实验。实验结果表明，与表现最好的模型相比，SAGN算法在Epinions数据集上的RMSE和MAE分别提升了2.14%、1.74%；在Ciao数据集上的RMSE和MAE分别提升了1.81%、1.79%。

关键词: 推荐算法, 社交网络, 注意力机制, 知识图谱

JIN Haibo, FENG Yujing. Social Knowledge-Aware Network Recommendation Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(4): 1105-1114.

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.

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