45 citations · 123 across the 6 of their papers we have counts for
6 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…
A Large Language Model Enhanced Conversational Recommender System
Yue Feng, Shuchang Liu, Zhenghai Xue +5
Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference…
Generative Flow Network for Listwise Recommendation
Shuchang Liu, Qingpeng Cai, Zhankui He +5
Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches th…
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.…