Schedule
MINDS Seminar Series | Won-Yong Shin (Yonsei University) - Graph Neural Networks and Their Applications to Learning of Recommender Systems
period : 2022-10-18 ~ 2022-10-18
time : 17:00:00 ~ 18:00
개최 장소 : Online streaming (Zoom)
Topic : Graph Neural Networks and Their Applications to Learning of Recommender Systems
개요
| Date | 2022-10-18 ~ 2022-10-18 | Time | 17:00:00 ~ 18:00 |
| Speaker | Won-Yong Shin | Affiliation | Yonsei University |
| Place | Online streaming (Zoom) | Streaming link | ID : 688 896 1076 / PW : 54321 |
| Topic | Graph Neural Networks and Their Applications to Learning of Recommender Systems | ||
| Contents | Recommender systems have been widely advocated as a way of providing suitable recommendation solutions to customers in various fields such as e-commerce, advertising, and social media sites. In recent years, thanks to the highly expressive capability of graph neural networks (GNNs) for effective representation learning of graphs, GNN-based recommender systems have been developed for improving the recommendation accuracy while exhibiting the state-of-the-art performance. In this talk, I first review existing GNN models for top-K recommendation, which generally were developed using implicit feedback by regarding observed user–item interactions as positive relations. More precisely, I describe state-of-the-art GNN-based recommender systems such as NGCF and LightGCN, which apply convolutions to graph domains, while performing layer aggregation to solve the oversmoothing problem. Then, I address some challenges such that existing GNN-based recommender systems overlook the existence of negative feedback due to their ease of modeling. To tackle this challenge, we discuss how to make use of low rating scores for representing users’ preferences since low ratings can still be informative in designing recommender systems. As a practical solution, I present SiReN, a new Sign-aware Recommender system based on GNN models and its performance superiority over state-of-the-art methods through comprehensive experiments. | ||
MinDS
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2022-10-04 10:19 ·
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