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模型以提升所有推荐场景的性能，近年来备受关注。然而，当前MSR研究面临两大阻碍领域发展的挑战：一是缺乏多场景数据集处理的统一流程，从而妨碍了公平比较；二是大多数模型未开源，导致与当前SOTA模型的对比变得复杂。为此，我们提出了基准框架 **Scenario-Wise Rec**，它包含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，我们利用一个工业广告数据集验证了该基准，进一步强化了其在真实场景中的可靠性与适用性。我们希望此基准能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，从而促进MSR领域的协作研究生态。我们的源代码也已公开。