RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System.
Year: 2021
Type: preprint
Abstract: Reinforcement learning based recommender systems (RL-based RS) aims at learning a good policy from a batch of collected data, with casting sequential recommendation to multi-step decision-making tasks. However, current RL-based RS benchmarks commonly have a large reality gap, because they involve artificial RL datasets or semi-simulated RS datasets, and the trained policy is directly evaluated in the simulation environment. In real-world situations, not all recommendation problems are suitable to be transformed into reinforcement learning problems. Unlike previous academic RL researches, RL-based RS suffer from extrapolation error and the difficulties of being well validated before deployment. In this paper, we introduce the RL4RS (Reinforcement Learning for Recommender Systems) benchmark - a new resource fully collected from industrial applications to train and evaluate RL algorithms with special concerns on the above issues. It contains two datasets, tuned simulation environments, related advanced RL baselines, data understanding tools, and counterfactual policy evaluation algorithms. The RL4RS suit can be found at this https URL. In addition to the RL-based recommender systems, we expect the resource to contribute to research in reinforcement learning and neural combinatorial optimization.
Source: arXiv (Cornell University)
Authors: Kai Wang, Zhene Zou, Qilin Deng, Yue Shang, Minghao Zhao, Runze Wu, Xudong Shen, Tangjie Lyu, Changjie Fan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3205450119