1 citations · 2 across the 10 of their papers we have counts for
11 papers
WatchLens: A Configurable Platform for Online Video Recommendation Experiments
Deogyong Kim, Dongha Lee
Studying how video recommender systems shape user behavior requires online experiments that link playback behavior with the recommendation conditions that produced it. Existing use…
Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions
Jeongeun Lee, Chanyoung Park, Dongha Lee
Multimodal large language model (MLLM)-based embodied agents have shown strong potential for solving complex tasks in physical environments. However, personalized assistance requir…
Will It Go Viral? Grounding Micro-Video Popularity Prediction on the Open Web
Ryang Heo, Dongha Lee
Micro-video popularity prediction (MVPP) forecasts the popularity a newly uploaded short-form video will attract within a fixed number of days after upload. This task supports down…
PAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimization
Wonjoong Kim, Yeonjun In, Sangwu Park +2
A significant hurdle for current LLMs is the execution of complex, multi-stage tasks. Group Relative Policy Optimization (GRPO) has been emerging as a leading choice, but its relia…
Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing
Deogyong Kim, Junseong Lee, Jeongeun Lee +4
Recent advances in large language models (LLMs) offer new opportunities for recommender systems by capturing the nuanced semantics of user interests and item characteristics throug…
Personalized Reward Modeling for Text-to-Image Generation
Jeongeun Lee, Ryang Heo, Dongha Lee
Recent text-to-image (T2I) models generate semantically coherent images from textual prompts, yet evaluating how well they align with individual user preferences remains an open ch…
Can Large Language Models be Effective Online Opinion Miners?
Ryang Heo, Yongsik Seo, Junseong Lee +1
The surge of user-generated online content presents a wealth of insights into customer preferences and market trends. However, the highly diverse, complex, and context-rich nature…
Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action
Tongyoung Kim, Jeongeun Lee, Soojin Yoon +2
Conversational Recommender Systems (CRSs)aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving i…
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
Sangam Lee, Ryang Heo, SeongKu Kang +1
Existing dense retrieval models struggle with reasoning-intensive retrieval task as they fail to capture implicit relevance that requires reasoning beyond surface-level semantic in…
Towards Unified and Adaptive Cross-Domain Collaborative Filtering via Graph Signal Processing
Jeongeun Lee, Seongku Kang, Won-Yong Shin +3
Collaborative Filtering (CF) is a foundational approach in recommender systems, but it struggles with challenges such as data sparsity and the cold-start problem. Cross-Domain Reco…
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
Jeongwhan Choi, Hyowon Wi, Chaejeong Lee +3
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from ra…