# Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark

arXiv: https://arxiv.org/abs/2412.17374 · HTML: https://mlsanity.com/paper/2412.17374/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [TAPO: Task-Referenced Adaptation for Prompt Optimization](https://mlsanity.com/paper/2501.06689/) (2025)
- [MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization](https://mlsanity.com/paper/2602.07520/) (2026)
- [TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation](https://mlsanity.com/paper/2605.13433/) (2026)

## Abstract (arXiv)

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, 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.