@inproceedings{fdba364495cd46198e7aae011e822114,
title = "GReS: Workshop on graph neural networks for recommendation and search",
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.",
keywords = "Graph neural networks, Information retrieval, Recommendation",
author = "Thibaut Thonet and St{\'e}phane Clinchant and Carlos Lassance and Elvin Isufi and Jiaqi Ma and Yutong Xie and Renders, {Jean Michel} and Michael Bronstein",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 15th ACM Conference on Recommender Systems, RecSys 2021 ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2021",
month = sep,
day = "13",
doi = "10.1145/3460231.3470937",
language = "English (US)",
series = "RecSys 2021 - 15th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery",
pages = "780--782",
booktitle = "RecSys 2021 - 15th ACM Conference on Recommender Systems",
address = "United States",
}