  Reinforcement Learning (RL)-based recommender systems (RSs) have garnered
considerable attention due to their ability to learn optimal recommendation
policies and maximize long-term user rewards. However, deploying RL models
directly in online environments and generating authentic data through A/B tests
can pose challenges and require substantial resources. Simulators offer an
alternative approach by providing training and evaluation environments for RS
models, reducing reliance on real-world data. Existing simulators have shown
promising results but also have limitations such as simplified user feedback,
lacking consistency with real-world data, the challenge of simulator
evaluation, and difficulties in migration and expansion across RSs. To address
these challenges, we propose KuaiSim, a comprehensive user environment that
provides user feedback with multi-behavior and cross-session responses. The
resulting simulator can support three levels of recommendation problems: the
request level list-wise recommendation task, the whole-session level sequential
recommendation task, and the cross-session level retention optimization task.
For each task, KuaiSim also provides evaluation protocols and baseline
recommendation algorithms that further serve as benchmarks for future research.
We also restructure existing competitive simulators on the KuaiRand Dataset and
compare them against KuaiSim to future assess their performance and behavioral
differences. Furthermore, to showcase KuaiSim's flexibility in accommodating
different datasets, we demonstrate its versatility and robustness when
deploying it on the ML-1m dataset.
