Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention. However, current research in MSR faces two significant challenges that hinder the field's development: the absence of uniform procedures for multi-scenario dataset processing, thus hindering fair comparisons, and most models being closed-sourced, which complicates comparisons with current SOTA models. Consequently, we introduce our benchmark, \textbf{Scenario-Wise Rec}, which comprises 6 public datasets and 12 benchmark models, along with a training and evaluation pipeline. Additionally, we validated the benchmark using an industrial advertising dataset, reinforcing its reliability and applicability in real-world scenarios. We aim for this benchmark to offer researchers valuable insights from prior work, enabling the development of novel models based on our benchmark and thereby fostering a collaborative research ecosystem in MSR. Our source code is also publicly available.

翻译：多场景推荐任务旨在构建一个统一的模型以提升所有推荐场景的性能，近来备受关注。然而，当前多场景推荐研究面临两大挑战，阻碍了该领域的发展：一是缺乏多场景数据集处理的统一流程，导致公平比较难以实现；二是多数模型未开源，使得与当前SOTA模型的对比变得复杂。为此，我们提出了基准框架 \textbf{Scenario-Wise Rec}，其中包含6个公共数据集和12个基准模型，以及一套完整的训练与评估流程。此外，我们使用一个工业广告数据集对该基准进行了验证，进一步增强了其在真实场景中的可靠性与适用性。我们希望该基准能够为研究人员提供来自先前工作的宝贵洞见，使其能够基于我们的基准开发新型模型，从而推动多场景推荐领域协作研究生态的发展。我们的源代码也已公开。