Abstract: Recommendation system can help users quickly find useful information and improve the retrieval efficiency of users effectively. However, the recommendation system has problems such as data sparsity and cold start, most of the existing recommendation algorithms that integrate social relations ignore the sparsity of social relations data, and there are few recommendation algorithms that integrate social relations and item attribute data at the same time. This paper proposes a recommendation model that is multi-task feature learning approach for social relationship and knowledge graph enhanced recommendation (MSAKR) in response to solve the above problems. Firstly, the algorithm extracts the user’s social relations through the graph convolutional neural network to get the user’s feature vector, then selects the neighbor by the graph centrality, and generates the virtual neighbor by the word2vec model, so as to alleviate the sparsity of the social data. This paper uses the attention mechanism to gather the neighbors. Secondly, multi-task learning and semantic-based matching model are used to extract the information of attribute knowledge graph to obtain the feature vector of the item. Finally, comprehensive recommendation is made to the user based on the obtained user and item feature vectors. In order to assess the performance of the recommendation algorithm, experiments are carried out on real datasets Douban and Yelp. Click-through rate predi-ction and Top-K recommendation are used to evaluate the performance of the model respectively. Experimental results show that the proposed model is superior to other benchmark models.

Key words: recommendation algorithm, graph convolutional neural network, social network, knowledge graph, multi-task learning

摘要： 推荐系统可以帮助用户快速发现有用信息，有效提高用户的检索效率，然而推荐系统存在数据稀疏性、冷启动等问题，现有的融合了社交关系的推荐算法大多忽略了社交关系数据的稀疏性，且同时融合社交关系和物品属性数据的推荐算法较少。为解决这方面的问题，提出了一种融合社交关系和知识图谱的推荐算法（MSAKR）。首先，该算法通过图卷积神经网络提取用户的社交关系得到用户的特征向量，采用图中心性筛选邻居，采用word2vec模型思想生成虚拟邻居，从而缓解社交数据的稀疏性，采用注意力机制来聚集邻居；其次，采用多任务学习和基于语义的匹配模型来提取物品属性知识图谱信息得到物品的特征向量；最后，根据得到的用户和物品特征向量向用户综合推荐。为验证提出算法的性能，在真实数据集豆瓣和Yelp上进行实验验证，分别使用点击率预测和[Top-K]推荐来评估模型性能，实验结果表明，提出的模型优于其他的基准模型。

关键词: 推荐算法, 图卷积神经网络, 社交网络, 知识图谱, 多任务学习