GReS

Workshop on graph neural networks for recommendation and search

Conference paper (2021)

Authors

Thibaut Thonet Naver Labs Europe France
Stéphane Clinchant Naver Labs Europe France
Carlos Lassance Naver Labs Europe France
Jiaqi Ma University of Michigan
Yutong Xie University of Michigan
Jean Michel Renders Naver Labs Europe France
Michael Bronstein Imperial College London
Copyright: © 2021 Thibaut Thonet, Stéphane Clinchant, Carlos Lassance, E. Isufi, Jiaqi Ma, Yutong Xie, Jean Michel Renders, Michael Bronstein
DOI: https://doi.org/10.1145/3460231.3470937
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Published Date

2021

Language

English

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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.

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RECSYS_GNN_WK.pdf
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3460231.3470937.pdf
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