1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.IR2025
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation
Wenyu Mao, Shuchang Liu, Haoyang Liu +3
Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items. E…
cs.IR2025★ 1 cited
Value Function Decomposition in Markov Recommendation Process
Xiaobei Wang, Shuchang Liu, Qingpeng Cai +4
Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adop…
cs.IR2025
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer
Chongming Gao, Kexin Huang, Ziang Fei +6
Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. C…