【摘要】 在缺乏用户交互互补项目方面数据的情况下，将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中，提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法，在用户历史交互项目集中推测用户交互的互补项目，基于知识图谱提取用户对互补项目的偏好，利用图像与文本学习项目之间的互补关系，最后基于神经网络实现两者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比，ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系，提升了推荐性能。更多还原

【Abstract】 In the absence of data on user interaction complementary items, this paper integrated user preferences for items into complementary item recommendation that only considered item relationships to improve the performance of the recommendation model.This paper proposed a method for recommending complementary items based on a knowledge graph, inferring the complementary items of user interaction in the user’s historical interaction items, extracting the user’s preference for complementary items based on the knowledge graph, and using the difference between image and text learning items complementary relationship, and finally realized the common learning based on neural network.Compared with the sub-optimal baseline method on the Amazon data set, the proposed method has a 7% increase in ACC and a 3% increase in precision, which shows that the performance of the proposed method is excellent.The method in this paper jointly learns the complementary relationship between the user’s preference for items and the items, and improves the recommendation performance.更多还原