45 citations · 116 across the 11 of their papers we have counts for
6 papers · 1 filter
KuaiSim: A Comprehensive Simulator for Recommender Systems
Kesen Zhao, Shuchang Liu, Qingpeng Cai +5
Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-ter…
Multi-Task Recommendations with Reinforcement Learning
Ziru Liu, Jiejie Tian, Qingpeng Cai +8
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard t…
Exploration and Regularization of the Latent Action Space in Recommendation
Shuchang Liu, Qingpeng Cai, Bowen Sun +7
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Wanqi Xue, Qingpeng Cai, Zhenghai Xue +6
Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric,…
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
Ruohan Zhan, Changhua Pei, Qiang Su +5
Watch-time prediction remains to be a key factor in reinforcing user engagement via video recommendations. It has become increasingly important given the ever-growing popularity of…
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Wanqi Xue, Qingpeng Cai, Ruohan Zhan +4
Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dw…