Welcome to MINDS!

Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

News

🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

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.