Single-shot Feature Selection for Multi-task Recommendations

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

5 Scopus Citations
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Author(s)

  • Zhaocheng Du
  • Bo Chen
  • Huifeng Guo
  • Ruiming Tang
  • Zhenhua Dong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationSIGIR '23
Subtitle of host publicationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages341–351
ISBN (Print)978-1-4503-9408-6
Publication statusPublished - 2023

Conference

Title46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)
LocationHybrid, Taipei International Convention Center
PlaceTaiwan
CityTaipei
Period23 - 27 July 2023

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-backwards 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 reproducibility1,2. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Research Area(s)

  • Feature Selection, Multi-task Learning, Recommender Systems

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Single-shot Feature Selection for Multi-task Recommendations. / Wang, Yejing; Du, Zhaocheng; Zhao, Xiangyu et al.
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY: Association for Computing Machinery, 2023. p. 341–351.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review