构建首个多场景推荐基准，解决数据与模型不透明问题。
Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
- 整合6个公开数据集和12个基准模型，统一评测流程。
- 在工业广告数据上验证，证明可应用于真实场景。
- 开源代码，推动多场景推荐领域协作研究。
多场景推荐（MSR）旨在构建统一模型以提升各推荐场景下的性能，近年来备受关注。然而，当前研究面临两大挑战：缺乏统一的多场景数据处理流程，导致评估不公平；多数模型闭源，难以与当前最优模型进行对比。为此，我们提出基准测试框架 Scenario-Wise Rec，包含6个公开数据集、12个基准模型及完整的训练与评估流程。此外，我们使用工业广告数据集验证了该基准的可靠性与实际应用价值。本基准旨在为研究者提供来自已有工作的宝贵洞见，支持基于此构建新模型，从而促进多场景推荐领域的协同研究生态。相关源代码已公开。
原文摘要 · Abstract (English)
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.
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