research-article

Single-shot Feature Selection for Multi-task Recommendations

Authors:

Zhenhua DongAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 341 - 351

https://doi.org/10.1145/3539618.3591767

Published: 18 July 2023 Publication History

Abstract

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety of real-world applications due to their exceptional training efficiency and recommendation quality. However, conventional MTRSs often input all relevant feature fields without distinguishing their contributions to different tasks, which can lead to confusion and a decline in performance. Existing feature selection methods may neglect task relations or require significant computation during model training in multi-task setting. To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. Specifically, MultiSFS first efficiently obtains task-specific feature importance through a single forward-backward pass. Then, a data-task bipartite graph is constructed to learn field-level task relations. Subsequently, MultiSFS merges the feature importance according to task relations and selects feature fields for different tasks. To demonstrate the effectiveness and properties of MultiSFS, we integrate it with representative MTRS models and evaluate on three real-world datasets. The implementation code is available online to ease reproducibility.

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Index Terms

Single-shot Feature Selection for Multi-task Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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Huawei (Huawei Innovation Research Program)
APRC - CityU New Research Initiatives
SIRG - CityU Strategic Interdisciplinary Research Grant
CityU - HKIDS Early Career Research Grant

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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https://dl.acm.org/doi/10.1145/3706631
Du ZWu CJia QZhu JChen X(2024)A Tutorial on Feature Interpretation in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687094(1281-1282)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687094
Jiang SZhu GWang YYuan CHuang YBaeza-Yates RBonchi F(2024)Automatic Multi-Task Learning Framework with Neural Architecture Search in RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671715(1290-1300)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671715
Jia PWang YDu ZZhao XWang YChen BWang WGuo HTang RBaeza-Yates RBonchi F(2024)ERASE: Benchmarking Feature Selection Methods for Deep Recommender SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671571(5194-5205)Online publication date: 25-Aug-2024
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Wang YXu DZhao XMao ZXiang PYan LHu YZhang ZWei XLiu Q(2024)Bi-Level User Modeling for Deep Recommenders2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00058(510-519)Online publication date: 9-Dec-2024
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