Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.

本文提出了利用知识图谱的路径循环神经网络模型（Knowledge-aware Path Recurrent Network），该模型可以通过组合实体和关系的语义生成路径表示，并利用路径内的序列依赖关系有效地推理用户 - 物品交互的基本原因。通过设计一种新的加权汇集操作，区分连接用户和物品的不同路径的强度，为模型赋予一定水平的可解释性。该模型在电影和音乐的两个数据集上进行了广泛的实验，显示出比现有的解决方案 Collaborative Knowledge Base Embedding 和 Neural Factorization Machine 更显著的改进效果。