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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…
cs.IR2024
Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning
Amit Sharma, Hua Li, Xue Li +1
Given an input query, a recommendation model is trained using user feedback data (e.g., click data) to output a ranked list of items. In real-world systems, besides accuracy, an im…