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cs.IR2025
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…
cs.IR2025
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…