TY - GEN
T1 - Scenario-Wise Rec
T2 - 34th ACM International Conference on Information and Knowledge Management, CIKM 2025
AU - Li, Xiaopeng
AU - Gao, Jingtong
AU - Jia, Pengyue
AU - Zhao, Xiangyu
AU - Wang, Yichao
AU - Wang, Wanyu
AU - Wang, Yejing
AU - Wang, Yuhao
AU - Guo, Huifeng
AU - Tang, Ruiming
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/11/10
Y1 - 2025/11/10
N2 - Multi-Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained considerable 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-source, which complicates comparisons with current SOTA models. Consequently, we introduce our benchmark, Scenario-Wise Rec, which comprises six public datasets and twelve baseline models, along with a training and evaluation pipeline. We further validate Scenario-Wise Rec on an industrial advertising dataset, underscoring its robustness. We hope the benchmark will give researchers clear insights into prior work, enabling them to develop novel models and thereby fostering a collaborative research ecosystem in MSR. Our source code is publicly available (https://github.com/Applied-Machine-Learning-Lab/Scenario-Wise-Rec).
AB - Multi-Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained considerable 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-source, which complicates comparisons with current SOTA models. Consequently, we introduce our benchmark, Scenario-Wise Rec, which comprises six public datasets and twelve baseline models, along with a training and evaluation pipeline. We further validate Scenario-Wise Rec on an industrial advertising dataset, underscoring its robustness. We hope the benchmark will give researchers clear insights into prior work, enabling them to develop novel models and thereby fostering a collaborative research ecosystem in MSR. Our source code is publicly available (https://github.com/Applied-Machine-Learning-Lab/Scenario-Wise-Rec).
KW - ctr prediction
KW - multi scenario recommendation
KW - recommendation systems
UR - https://www.scopus.com/pages/publications/105023159841
U2 - 10.1145/3746252.3761137
DO - 10.1145/3746252.3761137
M3 - 会议稿件
AN - SCOPUS:105023159841
T3 - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
SP - 1685
EP - 1695
BT - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery, Inc
Y2 - 10 November 2025 through 14 November 2025
ER -