research-article

Exploiting Group Information for Personalized Recommendation with Graph Neural Networks

Authors:

Yuanchun Jiang,

Mingyue ZhuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 27, Pages 1 - 23

https://doi.org/10.1145/3464764

Published: 27 September 2021 Publication History

Abstract

Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared

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Index Terms

Exploiting Group Information for Personalized Recommendation with Graph Neural Networks
1. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 27 September 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 2

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Major Program of the National Natural Science Foundation of China
Foundation for Innovative Research Groups of the National Natural Science Foundation of China
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities of China
National Engineering Laboratory for Big Data Distribution and Exchange Technologies

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https://dl.acm.org/doi/10.1145/3611385
Jing LSong XLin XZhao ZZhou WNie L(2023)Stylized Data-to-text Generation: A Case Study in the E-Commerce DomainACM Transactions on Information Systems10.1145/360337442:1(1-24)Online publication date: 18-Aug-2023
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Zhang HDuan K(2023)Research on Personalized Recommendation Algorithm for News on New Media Platforms2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN59264.2023.10402186(775-780)Online publication date: 22-Dec-2023
https://doi.org/10.1109/CICN59264.2023.10402186
Liu XYang LLiu ZLi XYang MWang CYu P(2023)Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386885(393-402)Online publication date: 15-Dec-2023
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Bai JHu BZhu MMao ZLiu A(2022)GroupDiff: Exploring A Unified Graph Structure and High-order Interactions for Group Recommendation2022 8th International Conference on Big Data Computing and Communications (BigCom)10.1109/BigCom57025.2022.00042(276-284)Online publication date: Aug-2022
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