基于知识图谱的用户表征及在互补产品推荐中的应用

互补产品推荐旨在为用户提供经常一起购买的产品，以满足共同的需求。现有的互补产品推荐方法大多考虑对产品的内容特性(视觉和文本内容)建模，而没有考虑用户购买产品的偏好。为此设计了一种融合用户偏好的互补产品推荐模型(complementary product recommendation models that integrate user preferences, CPRUP)。该模型首先计算产品之间图像和文本特征的互补关系；然后将知识图谱与注意力机制相结合，基于n-hop邻居挖掘用户历史购买产品之间的相关性，提出一种基于知识图谱的用户表征来提取用户对互补产品的偏好；最后基于神经网络实现互补关系与用户偏好的共同学习。使用Amazon数据集进行实验，提出的CPRUP模型与次优基线模型相比，ACC提升了5%,precision提升了4%,表明CPRUP模型可以更准确地为用户推荐互补产品。

User representation based on knowledge graph and its application in complementary product recommendation

Complementary product recommendation aims to provide users with products that are often purchased together to meet common needs. Existing complementary product recommendation methods mainly consider modeling the content characteristics(visual and textual content) of products, but do not consider the preferences of users when purchasing products. This paper designed a complementary product recommendation model that integrated user preferences, termed as CPRUP. The model firstly computed the complementarity of image and text features between products; then it combined knowledge graph with attention mechanism, mines the correlation between user''s historical purchased products based on n-hop neighbors, and proposed a user representation based on knowledge graph to extract user preferences for complementary products. Finally, it implemented joint learning of complementarity and user preferences based on neural networks. Experiments on the Amazon dataset show that the proposed CPRUP model has increased the ACC by 5% and the precision by 4% compared to the suboptimal baseline model, indicate that the CPRUP model can more accurately recommend complementary products to users.