GReS: Workshop on Graph Neural Networks for Recommendation and Search (Virtual Only)
GReS: Workshop on Graph Neural Networks for Recommendation and Search
Thibaut Thonet, Naver Labs Europe | Stéphane Clinchant, Naver Labs Europe | Carlos Lassance, NaverLabs Europe | Elvin Isufi, Delft University of Technology | Jiaqi Ma, University of Michigan | Yutong Xie, University of Michigan | Jean-Michel Renders, Naver Labs Europe | Michael Bronstein, Twitter
The longstanding paradigm of collaborative filtering in recommender systems posits that users with similar behavior tend to exhibit similar preferences. A graph formulation naturally arises from this view: the user-item interactions form a bipartite graph, which can be leveraged to refine recommendations by integrating similarities in users’ historical preferences. This perspective inspired numerous graph-based recommendation approaches in the pastRecently, the success brought about by deep learning led to the development of graph neural networks (GNNs). The key idea of GNNs is to propagate high-order information in the graph so as to learn representations which are similar for a node and its neighborhood. GNNs were initially applied to traditional machine learning problems such as classification or regression and later to recommendation and search. GNNs have in particular led to a new state of the art in top-k recommendation and next-item recommendation.The GReS workshop on Graph Neural Networks for Recommendation and Search is then an endeavor to bridge the gap between the RecSys and GNN communities and promote inter-collaborations, creating a more attractive and dedicated space to foster GNN contributions to the RecSys domain.