融合知识图谱与图片特征的推荐模型

目前知识图谱研究主要面向信息检索、自然语言理解等领域，在推荐系统中融合知识图谱成为推荐领域学者广泛关注的问题。为了解决单一知识图谱忽略的丰富知识信息，该文对知识图谱进行多模态扩展，并提出一种融合知识图谱与图片特征的推荐模型(KG-I)。不同于其他基于知识图谱的推荐算法，该方法增加视觉嵌入、知识嵌入和结构嵌入去挖掘用户项目之间的隐式反馈信息。该模型利用深度游走模型(Deep Walk)捕获空间结构的方法和波纹网络模型(RippleNet)挖掘知识图谱的知识表达的思想，并且考虑图片对用户偏好的影响，有效地将信息进行融合，并在真实数据集上与其他模型实验比较，研究多种特征的影响，分析不同稀疏度数据下的表现。结果表明，融合知识图谱与图片特征的个性化推荐模型完全优于其他的对比算法并且有效缓解数据稀疏情况。

Recommendation Model by Integrating Knowledge Graph and Image Features

At present, the study of knowledge graph focuses mainly on information retrieval, natural language understanding and other fields. Integrating knowledge graph with recommendation system has been concerned by scholars in the field. In order to mine the rich information ignored in knowledge graph, the knowledge graph is extended to multimodal and a recommendation model that incorporates Knowledge Graph with Image (KG-I) features is proposed. Different from other recommendation algorithms, visual embedding, knowledge embedding and structure embedding are combined to capture implicit feedback between user-items. The Deep Walk is used to capture the spatial structure and the ideal of RippleNet to retain the semantic features of knowledge graph, and the effect of images on preference is considered to integrate information. Compared with other models on the real data set, the influence of various features is studied, and the performance of different sparsity data is analyzed. The results show that the personalized recommendation model based on knowledge graph and image features outperforms other algorithms and the data sparsity can be alleviated.