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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 2024Show all

7 papers · 1 filter

cs.IR2024

LLM-Powered User Simulator for Recommender System

Zijian Zhang, Shuchang Liu, Ziru Liu +6

User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerati…

cs.IR2024

Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation

Chang Meng, Chenhao Zhai, Xueliang Wang +6

With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommend…

cs.IR2024

DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems

Jiaju Chen, Chongming Gao, Shuai Yuan +3

The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recomm…

cs.IR2024★ 6 cited

Modeling User Retention through Generative Flow Networks

Ziru Liu, Shuchang Liu, Bin Yang +7

Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed…

cs.IR2024★ 25 cited

M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

Zijian Zhang, Shuchang Liu, Jiaao Yu +9

Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehen…

cs.IR2024★ 23 cited

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

Ziru Liu, Shuchang Liu, Zijian Zhang +6

In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-t…

cs.IR2024★ 9 cited

Future Impact Decomposition in Request-level Recommendations

Xiaobei Wang, Shuchang Liu, Xueliang Wang +6

In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term perform…