Knowledge Graph Attention Network Enhanced Sequential Recommendation

Xingwei Zhu, Pengpeng Zhao, Jiajie Xu, Junhua Fang, Lei Zhao, Xuefeng Xian, Zhiming Cui, Victor S. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Knowledge graph (KG) has recently been proved effective and attracted a lot of attentions in sequential recommender systems. However, the relations between the attributes of different entities in KG, which could be utilized to improve the performance, remain largely unexploited. In this paper, we propose an end-to-end Knowledge Graph attention network enhanced Sequential Recommendation (KGSR) framework to capture the context-dependency of sequence items and the semantic information of items in KG by explicitly exploiting high-order relations between entities. Specifically, our method first combines the user-item bipartite graph and the KG into a unified graph and encodes all nodes of the unified graph into vector representations with TransR. Then, a graph attention network recursively propagates the information of neighbor nodes to refine the embedding of nodes and distinguishes the importance of neighbors with an attention mechanism. Finally, we apply recurrent neural network to capture the user’s dynamic preferences by encoding user-interactive sequence items that contain rich auxiliary semantic information. Experimental results on two datasets demonstrate that KGSR outperforms the state-of-the-art sequential recommendation methods.

Original languageEnglish
Title of host publicationWeb and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
EditorsXin Wang, Rui Zhang, Young-Koo Lee, Le Sun, Yang-Sae Moon
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030602581
StatePublished - 2020
Event4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 - Tianjin, China
Duration: Sep 18 2020Sep 20 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12317 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020


  • Graph neural network
  • Knowledge graph
  • Sequential recommendation


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