Owing to the superiority of GNN in learning on graph data and its efficacy in
capturing collaborative signals and sequential patterns, utilizing GNN
techniques in recommender systems has gain increasing interests in academia and
industry. In this survey, we provide a comprehensive review of the most recent
works on GNN-based recommender systems. We proposed a classification scheme for
organizing existing works. For each category, we briefly clarify the main
issues, and detail the corresponding strategies adopted by the representative
models. We also discuss the advantages and limitations of the existing
strategies. Furthermore, we suggest several promising directions for future
researches. We hope this survey can provide readers with a general
understanding of the recent progress in this field, and shed some light on
future developments.
Beschreibung
[2011.02260] Graph Neural Networks in Recommender Systems: A Survey
%0 Generic
%1 wu2020graph
%A Wu, Shiwen
%A Sun, Fei
%A Zhang, Wentao
%A Cui, Bin
%D 2020
%K final thema:gnn4rec
%T Graph Neural Networks in Recommender Systems: A Survey
%U http://arxiv.org/abs/2011.02260
%X Owing to the superiority of GNN in learning on graph data and its efficacy in
capturing collaborative signals and sequential patterns, utilizing GNN
techniques in recommender systems has gain increasing interests in academia and
industry. In this survey, we provide a comprehensive review of the most recent
works on GNN-based recommender systems. We proposed a classification scheme for
organizing existing works. For each category, we briefly clarify the main
issues, and detail the corresponding strategies adopted by the representative
models. We also discuss the advantages and limitations of the existing
strategies. Furthermore, we suggest several promising directions for future
researches. We hope this survey can provide readers with a general
understanding of the recent progress in this field, and shed some light on
future developments.
@misc{wu2020graph,
abstract = {Owing to the superiority of GNN in learning on graph data and its efficacy in
capturing collaborative signals and sequential patterns, utilizing GNN
techniques in recommender systems has gain increasing interests in academia and
industry. In this survey, we provide a comprehensive review of the most recent
works on GNN-based recommender systems. We proposed a classification scheme for
organizing existing works. For each category, we briefly clarify the main
issues, and detail the corresponding strategies adopted by the representative
models. We also discuss the advantages and limitations of the existing
strategies. Furthermore, we suggest several promising directions for future
researches. We hope this survey can provide readers with a general
understanding of the recent progress in this field, and shed some light on
future developments.},
added-at = {2021-06-20T23:53:10.000+0200},
author = {Wu, Shiwen and Sun, Fei and Zhang, Wentao and Cui, Bin},
biburl = {https://www.bibsonomy.org/bibtex/2ff0fa348f77d5c5ddcc5a42a808a523a/kherud},
description = {[2011.02260] Graph Neural Networks in Recommender Systems: A Survey},
interhash = {774150a79cfaacf5cc1cff4f82f68a38},
intrahash = {ff0fa348f77d5c5ddcc5a42a808a523a},
keywords = {final thema:gnn4rec},
note = {cite arxiv:2011.02260},
timestamp = {2021-06-20T23:53:10.000+0200},
title = {Graph Neural Networks in Recommender Systems: A Survey},
url = {http://arxiv.org/abs/2011.02260},
year = 2020
}