Abstract: The current personalized recommendation platform has the characteristics of a wide range of data sources and many data types. With the data sparsity of the platform as an important reason for affecting the performance of the recommendation system, there are many challenges faced by the recommendation system:how to mine structured data and unstructured data of the platform to discover more features, improve the accuracy of recommendations in data-sparse scenarios, alleviate the cold start problem, and make recommendations interpretable. Therefore, for the personalized scenario of recommending Items for Users, the Heterogeneous Information Network (HIN) was used to build the association relationships between objects in the recommendation platform, and the Meta-Graph was used to describe the association paths between objects and calculate the User-Item similarity matrices under different paths; the FunkSVD matrix decomposition algorithm was adopted to calculate the implicit features of Users and Items, and for the unstructured data with text as an example, the Convolutional Neural Network (CNN) technology was used to mine the text features of the data; after splicing the features obtained by the two methods, a Factorization Machine (FM) incorporating historical average scores of Users and Items was used to predict Users' scores for Items. In the experiment, based on the public dataset Yelp, the proposed hybrid recommendation model, the single recommendation model based on Meta-Graph, the FM Recommendation model (FMR) and the FunkSVD based recommendation model were established and trained. Experimental results show that the proposed hybrid recommendation model has good validity and interpretability, and compared with the comparison models, the recommendation accuracy of this model has been greatly improved.

Key words: recommendation system, Heterogeneous Information Network (HIN), Meta-Graph, Convolutional Neural Network (CNN), Factorization Machine (FM)

摘要： 个性化推荐平台具有数据来源广泛且数据类型丰富的特点，而其中的数据稀疏是影响推荐系统性能的重要原因。如何挖掘推荐平台结构化数据和非结构化数据以发现更多特征，在数据稀疏场景中提高推荐的准确率，缓解冷启动问题，并且使得推荐具有可解释性，是推荐系统面临的重大挑战。因此，针对为User推荐Item的个性化场景，利用异构信息网络（HIN）构建推荐平台中对象间的关联关系，以元路径（Meta-Graph）描述对象间的关联路径并计算不同路径下的User-Item相似度矩阵；用FunkSVD矩阵分解算法分解User以及Item的隐式特征，并针对以文本为例的非结构化数据以卷积神经网络（CNN）技术挖掘这些数据的文本特征；将两种方式获取的特征进行拼接后，使用融入User和Item历史平均分的因子分解机（FM）来预测User对Item的评分。实验过程基于公开数据集Yelp建立提出的混合推荐模型、基于Meta-Graph的单一推荐模型、因子分解机推荐（FMR）模型以及基于FunkSVD推荐模型并对它们进行训练。实验结果表明，所提出的混合推荐模型具有较好的有效性和可解释性，相较于几个对比模型，该模型的推荐精度均有较大的提升。

关键词: 推荐系统, 异构信息网络, 元路径, 卷积神经网络, 因子分解机