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20222025
most citedMulti-Task Recommendations with Reinforcement Learning

45 citations · 197 across the 23 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

cs.IR2023★ 8 cited

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…

cs.IR2023★ 7 cited

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…

cs.IR2023★ 18 cited

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…

cs.LG2023★ 2 cited

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…

cs.IR2023★ 45 cited

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…

cs.LG2023★ 4 cited

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…

cs.IR2023★ 41 cited

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.…

cs.LG2023★ 2 cited

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…