45 citations · 116 across the 11 of their papers we have counts for
11 papers
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…
State Regularized Policy Optimization on Data with Dynamics Shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu +4
In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of cu…
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…
Reinforcing User Retention in a Billion Scale Short Video Recommender System
Qingpeng Cai, Shuchang Liu, Xueliang Wang +6
Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, the…
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.…
Two-Stage Constrained Actor-Critic for Short Video Recommendation
Qingpeng Cai, Zhenghai Xue, Chi Zhang +9
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially intera…
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…
Constrained Reinforcement Learning for Short Video Recommendation
Qingpeng Cai, Ruohan Zhan, Chi Zhang +5
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and…
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
Weicong Ding, Hanlin Tang, Jingshuo Feng +17
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the se…