Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917005787922432 |
|---|---|
| author | Li, Xiaopeng Gao, Jingtong Jia, Pengyue Zhao, Xiangyu Wang, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Zhao, Xiangyu Guo, Huifeng Tang, Ruiming |
| author_facet | Li, Xiaopeng Gao, Jingtong Jia, Pengyue Zhao, Xiangyu Wang, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Zhao, Xiangyu Guo, Huifeng Tang, Ruiming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17374 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark Li, Xiaopeng Gao, Jingtong Jia, Pengyue Zhao, Xiangyu Wang, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Zhao, Xiangyu Guo, Huifeng Tang, Ruiming Information Retrieval 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. |
| title | Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.17374 |