5 papers
MATE: Adaptive Long- and Short-Term User Memory for LLM-Based Recommendation
Yu Hou
Large language model (LLM)-enhanced recommender systems leverage rich item semantics to support personalized recommendation. However, semantic representations alone do not determin…
Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation
Yu Hou, Won-Yong Shin
Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item t…
Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization
Yu Hou, Hua Li, Ha Young Kim +1
Diffusion models recently emerged as a powerful paradigm for recommender systems, offering state-of-the-art performance by modeling the generative process of user-item interactions…
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
Yu Hou, Jin-Duk Park, Won-Yong Shin
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. H…
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty
Yu Hou, Cong Tran, Ming Li +1
In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy co…