5 papers
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…
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…
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…
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…