Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark

Xiaopeng Li; Jingtong Gao; Pengyue Jia; Xiangyu Zhao; Yichao Wang; Wanyu Wang; Yejing Wang; Yuhao Wang; Huifeng Guo; Ruiming Tang

arXiv · 2412.17374

Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark

Xiaopeng Li·Jingtong Gao·Pengyue Jia·Xiangyu Zhao·Yichao Wang·Wanyu Wang·Yejing Wang·Yuhao Wang·Huifeng Guo·Ruiming Tang

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Abstract

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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BibTeX RIS

Xiaopeng Li, Jingtong Gao, Pengyue Jia, Xiangyu Zhao, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Wang, Huifeng Guo, Ruiming Tang. 2024-12-23. Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark. https://arxiv.org/abs/2412.17374

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