GReS: Workshop on graph neural networks for recommendation and search

Thibaut Thonet, Stéphane Clinchant, Carlos Lassance, Elvin Isufi, Jiaqi Ma, Yutong Xie, Jean Michel Renders, Michael Bronstein

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Graph neural networks (GNNs) have recently gained significant momentum in the recommendation community, demonstrating state-of-the-art performance in top-k recommendation and next-item recommendation. Despite promising results on GNN-based recommendation and search, most of the current GNN research remains essentially concentrated on more traditional tasks such as classification or regression. The GReS workshop on Graph Neural Networks for Recommendation and Search is then a first endeavor to bridge the gap between the RecSys and GNN communities, and promote recommendation and search problems amongst GNN practitioners.

Original languageEnglish
Title of host publicationRecSys 2021 - 15th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery (ACM)
Pages780-782
Number of pages3
ISBN (Electronic)978-1-4503-8458-2
DOIs
Publication statusPublished - 2021
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands
Duration: 27 Sept 20211 Oct 2021

Publication series

NameRecSys 2021 - 15th ACM Conference on Recommender Systems

Conference

Conference15th ACM Conference on Recommender Systems, RecSys 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • Graph neural networks
  • Information retrieval
  • Recommendation

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