Abstract: Aiming at the problem of low accuracy of traditional social recommendation, this paper proposes a use consistent social recommendation model based on multi-view fusion. The social recommendation model takes into account the inconsistency of users in social networks and the influence of single view information on the recommendation results. It uses the attention mechanism to dynamically filter out inconsistent social neighbors, and combines user-item interaction information to learn user feature expression. At the same time, the feature representation of the project in low-dimensional space is learned from multiple views such as knowledge graph and user-project history interaction information. Finally, the characteristics of users and items are represented by inner product operation to complete the final recommendation task. In order to verify the effectiveness of the proposed recommendation algorithm, six baseline models are compared on two public datasets of Douban and Yelp, and the recall, normalized discounted cumulative gain (NDCG ) and precision are used as evaluation indicators. The experimental results show that the performance of the proposed social recommendation model is better than other models.

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

摘要： 针对传统社交推荐准确率不高的问题，提出一种基于多视图融合的用户一致性社交推荐模型。该社交推荐模型考虑到社交网络中用户的不一致性和单一视图信息对推荐结果的影响，使用注意力机制动态过滤出不一致的社交邻居，并结合用户-项目交互信息来学习用户特征表达；同时从知识图谱（knowledge graph，KG）、用户-项目历史交互信息等多个视图学习项目在低维空间的特征表示；最后将用户和项目的特征表示进行内积操作，从而完成最终的推荐任务。为了验证推荐算法的有效性，在Douban和Yelp两个公开的数据集上与六个基线模型进行对比实验，并采用召回率、归一化折损累计增益（normalized discounted cumulative gain，NDCG）和精确率作为评估指标，实验结果表明，所提出的社交推荐模型的性能优于其他模型。

关键词: 社交推荐, 知识图谱, 神经网络, 注意力机制