Integrating knowledge graph into deep neural network-based recommender system

Tongxuan Wang, Xiaolong Zheng, Saike He, Zhu Zhang, Desheng Dash Wu, Daniel Dajun Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, the integration of knowledge graphs into explainable recommendation systems has attracted more and more attention. And the relations existing in the knowledge graph can provide much information while exploring the users' preference. However, existing approaches only consider the single relation between entities, so they lack accuracy and practicality for multiple relations. In addition, previous work cannot capture the semantics of all paths. Towards this end, we propose three significant modelling advances: (1) besides the relations, we also learn to jointly reasoning on the entities and entity-types; (2) we use elaborate pooling layer to incorporate the paths between entities; (3) we take a better way to extract paths' semantic representations. The experimental study demonstrates the superiority of our method compared with the state-of-the-art ones.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Cybernetics, Cybconf 2019 - Proceedings
EditorsJin Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728100302
DOIs
StatePublished - Jul 5 2019
Event4th IEEE International Conference on Cybernetics, Cybconf 2019 - Beijing, China
Duration: Jul 5 2019Jul 7 2019

Publication series

NameIEEE International Conference on Cybernetics, Cybconf 2019 - Proceedings

Conference

Conference4th IEEE International Conference on Cybernetics, Cybconf 2019
Country/TerritoryChina
CityBeijing
Period7/5/197/7/19

Keywords

  • deep neural networks
  • explainable recommendation
  • knowledge graph

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Information Systems and Management

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