GCNNIRec: Graph Convolutional Networks with Neighbor Complex Interactions for Recommendation

Teng MEI, Tianhao SUN*, Renqin CHEN, Mingliang ZHOU, Leong Hou U

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

Abstract

In recent years, tremendous efforts have been made to explore features contained in user-item graphs for recommendation based on Graph Neural Networks (GNN). However, most existing recommendation methods based on GNN use weighted sum of directly-linked node’s features only, assuming that neighboring nodes are independent individuals, neglecting possible correlations between neighboring nodes, which may result in failure of capturing co-occurrence signals. Therefore, in this paper, we propose a novel Graph Convolutional Network with Neighbor complex Interactions for Recommendation (GCNNIRec) focused upon capturing possible co-occurrence signals between node neighbors. Specifically, two types of modules, the Linear-Aggregator module and the Interaction-Aggregator module are both inside GCNNIRec. The former module linearly aggregates the features of neighboring nodes to obtain the representation of target node. The latter utilizes the interactions between neighbors to aggregate the co-occurrence features of nodes to capture co-occurrence features. Furthermore, empirical results on three real datasets confirm not only the state-of-the-art performance of GCNNIRec but also the performance gains achieved by introducing Interaction-Aggregator module into GNN.

Original languageEnglish
Title of host publicationWeb and Big Data : 5th International Joint Conference, APWeb-WAIM 2021, Proceedings
EditorsLeong Hou U, Marc SPANIOL, Yasushi SAKURAL, Junying CHEN
PublisherSpringer, Cham
Pages338-347
Number of pages10
ISBN (Print)9783030858988
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021 - Guangzhou, China
Duration: 23 Aug 202125 Aug 2021

Publication series

NameLecture Notes in Computer Science
Volume12859
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021
Country/TerritoryChina
CityGuangzhou
Period23/08/2125/08/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Graph neural networks
  • Neighbor interactions
  • Recommender system

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