Yile Liang

RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System

2021 · arXiv (Cornell University)

Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large …

Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupled behaviors of …

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long …