Learning Intents behind Interactions with Knowledge Graph for Recommendation

Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Knowledge graph (KG) plays an increasingly important role in
recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However,
existing GNN-based models are coarse-grained in relational
modeling, failing to (1) identify user-item relation at a fine-grained
level of intents, and (2) exploit relation dependencies to preserve
the semantics of long-range connectivity.
In this study, we explore intents behind a user-item interaction
by using auxiliary item knowledge, and propose a new model,
Knowledge Graph-based Intent Network (KGIN). Technically, we
model each intent as an attentive combination of KG relations,
encouraging the independence of different intents for better
model capability and interpretability. Furthermore, we devise a
new information aggregation scheme for GNN, which recursively
integrates the relation sequences of long-range connectivity (i.e.,
relational paths). This scheme allows us to distill useful information
about user intents and encode them into the representations of users
and items. Experimental results on three benchmark datasets show
that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT [41], KGNN-LS [38], and CKAN [47].
Further analyses show that KGIN offers interpretable explanations
for predictions by identifying influential intents and relational
paths. The implementations are available at https://github.com/
huangtinglin/Knowledge_Graph_based_Intent_Network.
Original languageEnglish
Title of host publicationThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PublisherAssociation for Computing Machinery (ACM)
Pages878–887
Number of pages10
ISBN (Electronic)9781450383127
DOIs
Publication statusPublished - 19 Apr 2021

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Keywords

  • Graph Neural Networks
  • Knowledge Graph
  • Recommendation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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