Recurrent knowledge graph embedding for effective recommendation

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, Chi Xu

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

70 Citations (Scopus)

Abstract

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.

Original languageEnglish
Title of host publicationRecSys 2018 - 12th ACM Conference on Recommender Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages297-305
Number of pages9
ISBN (Electronic)9781450359016
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018

Conference

Conference12th ACM Conference on Recommender Systems, RecSys 2018
CountryCanada
CityVancouver
Period2/10/187/10/18

Keywords

  • Attention Mechanism
  • Knowledge Graph
  • Recurrent Neural Network
  • Semantic Representation

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