标题:KGAT: Knowledge graph attention network for recommendation
作者:Wang, Xiang ;He, Xiangnan ;Cao, Yixin ;Liu, Meng ;Chua, Tat-Seng
作者机构:[Wang, Xiang ;Cao, Yixin ;Chua, Tat-Seng ] National University of Singapore, Singapore;[Liu, Meng ] Shandong University, China;[He, Xiangnan ] Univers 更多
会议名称:25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
会议日期:4 August 2019 through 8 August 2019
来源:Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
出版年:2019
页码:950-958
DOI:10.1145/3292500.3330989
关键词:Collaborative Filtering; Embedding Propagation; Graph Neural Network; Higher-order Connectivity; Knowledge Graph; Recommendation
摘要:To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations - which connect two items with one or multiple linked attributes - are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node's neighbors (which can be users, items, or attributes) to refine the node's embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit high-order relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM [11] and RippleNet [29]. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism. We release the codes and datasets at https://github.com/xiangwang1223/knowledge_graph_attention_network. © 2019 Association for Computing Machinery.
收录类别:EI;SCOPUS
Scopus被引频次:8
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071195192&doi=10.1145%2f3292500.3330989&partnerID=40&md5=75b7ca64bcc92f7172bc0327251cb161
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