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 past
Recently, 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.
The GReS workshop accepts papers of up to 14 pages following the standard single-column ACM RecSys template. This length does not include references and reviewers will be asked to comment on whether the length is appropriate for the contribution. Submissions are double-blind (and therefore should be anonymized) and to be provided in a single file in .pdf format.
Submission link: https://cmt3.research.microsoft.com/GReS2021/
Paper submission deadline: July 29th, 2021 (AoE)
Author notification: August 21st, 2021 (AoE)
Camera-ready version deadline: September 4th, 2021 (AoE)
The topics relevant to the GReS workshop include (but are not limited to):
3:00 – 3:05 PM: Workshop’s Opening
3:05 – 3:45 PM: Keynote I – Graph Neural Networks for Recommendation by Xiang Wang (National University of Singapore)
3:45 – 3:50 PM: Short Break
3:50 – 4:10 PM: Accepted Paper I – Knowledge Graph Attention for Sequential Recommendations by Amjadi et al
4:10 – 4:50 PM: Keynote II – Towards GNN explainability: Random Walk Graph Neural Networks by Michalis Vazirgiannis (Ecole Polytechnique)
4:50 – 5:20 PM: Long Break on wonder.me
5:20 – 5:40 PM: Accepted Paper II – CoRGi: Content-Rich Graph Neural Networks with Attention by Kim et al
5:40 – 6:00 PM: Accepted Paper III – Referral prediction in Healthcare using Graph Neural Networks by Duarte et al
6:00 – 6:10 PM: Short Break
6:10 – 6:50 PM: Keynote III – Powering Pinterest Recommendations with Graph Neural Networks by Andrew Zhai (Pinterest)
6:50 – 7:00 PM: Workshop’s Closing
Xiang Wang (National University of Singapore)
Title: Graph Neural Networks for Recommendation
Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in many domains and shown great potentials in personalized recommendation. In this talk, I will give a brief introduction on why GNNs are suitable for recommendation, and how to incorporate GNNs into some recommendation scenarios (e.g., collaborative filtering, knowledge-aware recommendation). I will also share some points on what advances we are researching into (e.g., self-supervised learning, contrastive learning). In the last part, I will discuss in the “Achilles’ Heel” of the GNN-based recommender models (e.g., amplify popularity bias), as well as some possible solutions (e.g., causal inference).
Michalis Vazirgiannis (Ecole Polytechnique)
Title: Towards GNN explainability: Random Walk Graph Neural Networks
Abstract: In recent years, graph neural networks (GNNs) have become the de facto tool for performing machine learning tasks on graphs. Most GNNs belong to the family of message passing neural networks (MPNNs). These models employ an iterative neighborhood aggregation scheme to update vertex representations. Then, to compute vector representations of graphs, they aggregate the representations of the vertices using some permutation invariant function. One would expect the hidden layers of a GNN to be composed of parameters that take the form of graphs. However, this is not the case for MPNNs since their update procedure is parameterized by fully-connected layers. In this paper, we propose a more intuitive and transparent architecture for graph-structured data, so-called Random Walk Graph Neural Network (RWNN). The first layer of the model consists of a number of trainable “hidden graphs” which are compared against the input graphs using a random walk kernel to produce graph representations. These representations are then passed on to a fully-connected neural network which produces the output. The employed random walk kernel is differentiable, and therefore, the proposed model is end-to-end trainable. We demonstrate the model’s transparency on synthetic datasets. Furthermore, we empirically evaluate the model on graph classification datasets and show that it achieves competitive performance.
Andrew Zhai (Pinterest)
Title: Powering Pinterest Recommendations with Graph Neural Networks
Abstract: Pinterest is the home of inspiration to over 450M monthly active users. Inspiration comes from our personalized recommender systems generating content from our catalog of billions of ideas. Graph Neural Networks (GNNs) have been a key method in improving the predictive performance of these systems through helping us understand content, search queries, and users more comprehensively. In our talk we will discuss the evolution of these representations starting with (1) using GNNs to model content (pins) combining multimodal node features with our web scale pin-board graph of billions of nodes and edges (2) showing how our content embeddings enable us to learn good representations of search queries and users, combining our GNN embeddings with techniques such as sequence models to capture temporal behavior of users across all of Pinterest. Beyond sharing technical details, we will quantify our learnings with online experimentation showing the impact of our methods.
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