Social Recommendation via Graph Attentive Aggregation

Yuanwei Liufu, Hong Shen

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


Recommender systems play an important role in helping users discover items of interest from a large resource collection in various online services. Although deep graph neural network-based collaborative filtering methods have achieved promising performance in recommender systems, they are still some weaknesses. Firstly, existing graph neural network methods only take user-item interactions into account neglecting direct user-user interactions which can be obtained from social networks. Secondly, they treat the observed data uniformly without considering fine-grained differences in importance or relevance in the user-item interactions. In this paper, we propose a novel graph neural network social graph attentive aggregation (SGA) which is suitable for parallel training to boost efficiency which is the common bottleneck for neural network deployed machine learning models. This model obtains user-user collaborative information from social networks and utilizes self-attention mechanism to model the differentiation of importance in the user-item interactions. We conduct experiments on two real-world datasets and the results demonstrate that our method is effective and can be trained in parallel efficiently.

Original languageEnglish
Title of host publicationParallel and Distributed Computing, Applications and Technologies - 22nd International Conference, PDCAT 2021, Proceedings
EditorsHong Shen, Yingpeng Sang, Yong Zhang, Nong Xiao, Hamid R. Arabnia, Geoffrey Fox, Ajay Gupta, Manu Malek
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783030967710
Publication statusPublished - 2022
Externally publishedYes
Event22nd International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2021 - Guangzhou, China
Duration: 17 Dec 202119 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13148 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2021


  • Graph neural network
  • Parallel computing
  • Recommendation system
  • Social recommendation


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