融合社交网络用户相似度的社会化推荐
Social Recommendation with Social Network Users Similarity
DOI: 10.12677/CSA.2021.111003, PDF,    科研立项经费支持
作者: 邓志彬, 陈平华：广东工业大学计算机学院，广东 广州；熊建斌：广东技术师范大学自动化学院，广东 广州
关键词: 社会化推荐；社交网络用户相似度；矩阵分解；图卷积神经网络；Social Recommendation； Social Network Users Similarity； Matrix Factorization； Graph Convolutional Neural Network
摘要: 针对传统的社会化推荐准确率不高问题，在综合考虑社交网络子图拓扑、用户信任和用户评分相似性等社交网络用户相似度影响因素的基础上，提出了一种融合社交网络用户相似度的社会化推荐算法SRSUS。算法以传统矩阵分解为框架，首先使用图卷积神经网络对用户社交网络进行学习得到包含社交网络子图拓扑结构和连接关系的用户潜在特征，然后利用社交关系计算用户社会信任度，接着利用评分数据计算用户评分相似性，最后综合使用用户潜在特征、用户信任度和用户评分相似性计算社交网络用户相似度并将其融入用户评分矩阵分解中，以此预测用户对预测项目的评分值。Filmtrust、Ciao和Epinions等公开数据集上的实验结果表明：本文算法普遍优于其他的社会化推荐算法。
Abstract: In view of the low accuracy of traditional social recommendation, a social recommendation algorithm SRSUS integrating social network user similarity was proposed based on the comprehensive consideration of social network user similarity factors, such as subgraph topology, user trust and user rating similarity. The algorithm takes traditional matrix decomposition as the framework. Firstly, the graph convolutional neural network is used to learn the user’s social network to obtain the user’s potential characteristics including the topology structure and connection relations of the social network subgraph. Then social relation is used to calculate user’s social trust and score data is used to calculate the user rating similarity. Finally, user potential characteristics, user trust and user rating similarity are comprehensively used to calculate social network user similarity and then integrate it into user rating matrix decomposition. In this way, the user's rating of the predicted item can be predicted. Experimental results on Epinions, Filmtrust, Ciao and other public data sets show that this algorithm is generally superior to other social recommendation algorithms.
文章引用：邓志彬, 陈平华, 熊建斌. 融合社交网络用户相似度的社会化推荐[J]. 计算机科学与应用, 2021, 11(1): 19-27. https://doi.org/10.12677/CSA.2021.111003

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