TY - JOUR AU - Wang, Xiang AU - Wang, Dingxian AU - Xu, Canran AU - He, Xiangnan AU - Cao, Yixin AU - Chua, Tat-Seng PY - 2019/07/17 Y2 - 2024/03/28 TI - Explainable Reasoning over Knowledge Graphs for Recommendation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33015329 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4470 SP - 5329-5336 AB - <p>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.</p><p>In this paper, we contribute a new model named <em>Knowledgeaware Path Recurrent Network</em> (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 <em>Collaborative Knowledge Base Embedding</em> and <em>Neural Factorization Machine</em>.</p> ER -