GReS
Workshop on graph neural networks for recommendation and search
Thibaut Thonet (Naver Labs Europe France)
Stéphane Clinchant (Naver Labs Europe France)
Carlos Lassance (Naver Labs Europe France)
Elvin Isufi (Multimedia Computing)
Jiaqi Ma (University of Michigan)
Yutong Xie (University of Michigan)
Jean Michel Renders (Naver Labs Europe France)
Michael Bronstein (Imperial College London)
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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.