6 citations · 8 across the 10 of their papers we have counts for
10 papers
When Language Learners Code-Switch: Korean-English Code-Switching in LLM-based Speaking Practice
Junyeong Park, Jieun Han, Yeon Su Park +5
Code-switching (CSW), the use of multiple languages within the same interaction, has been widely studied in NLP for multilingual communication. While CSW also occurs in language-le…
FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API
Juhyeong Kim, Yejin Kim, Youngbin Lee +1
We present FinAI Data Assistant, a practical approach for natural-language querying over financial databases that combines large language models (LLMs) with the OpenAI Function Cal…
GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents
Yejin Kim, Youngbin Lee, Juhyeong Kim +1
This study demonstrates that GuruAgents, prompt-guided AI agents, can systematically operationalize the strategies of legendary investment gurus. We develop five distinct GuruAgent…
LLM-Enhanced Black-Litterman Portfolio Optimization
Youngbin Lee, Yejin Kim, Juhyeong Kim +2
The Black-Litterman model addresses the sensitivity issues of tra- ditional mean-variance optimization by incorporating investor views, but systematically generating these views re…
Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization
Juhyeong Kim, Sungyoon Choi, Youngbin Lee +3
We propose Decision by Supervised Learning (DSL), a practical framework for robust portfolio optimization. DSL reframes portfolio construction as a supervised learning problem: mod…
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…
NFTs to MARS: Multi-Attention Recommender System for NFTs
Seonmi Kim, Youngbin Lee, Yejin Kim +2
Recommender systems have become essential tools for enhancing user experiences across various domains. While extensive research has been conducted on recommender systems for movies…