43 citations · 109 across the 7 of their papers we have counts for
3 papers · 1 filter
A Self-Correcting Sequential Recommender
Yujie Lin, Chenyang Wang, Zhumin Chen +6
Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendati…
On the User Behavior Leakage from Recommender System Exposure
Xin Xin, Jiyuan Yang, Hanbing Wang +6
Modern recommender systems are trained to predict users potential future interactions from users historical behavior data. During the interaction process, despite the data coming f…
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
Xin Xin, Tiago Pimentel, Alexandros Karatzoglou +3
Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become…