6 citations · 8 across the 10 of their papers we have counts for
4 papers · 1 filter
A Recommender System for NFT Collectibles with Item Feature
Minjoo Choi, Seonmi Kim, Yejin Kim +3
Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movi…
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling
Youngbin Lee, Yejin Kim, Javier Sanz-Cruzado +2
Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even ad…
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks
Yejin Kim, Youngbin Lee, Minyoung Choe +2
This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We…
A Temporal Graph Network Framework for Dynamic Recommendation
Yejin Kim, Youngbin Lee, Vincent Yuan +2
Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. Afte…