26 citations · 26 across the 2 of their papers we have counts for
2 papers
cs.IR2023
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
Zhaochun Ren, Na Huang, Yidan Wang +7
Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumul…
cs.IR2023★ 26 cited
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…