Search Results for author:
Found 3 papers, 2 papers with code
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
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-term rewards.
KuaiSim: A Comprehensive Simulator for Recommender Systems
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
User Retention-oriented Recommendation with Decision Transformer
However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
