AdaFS : Adaptive Feature Selection in Deep Recommender System

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

14 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Title of host publicationKDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages3309–3317
ISBN (Print)978-1-4503-9385-0
Publication statusPublished - Aug 2022

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Title28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022)
LocationWashington DC Convention Center
PlaceUnited States
CityWashington DC
Period14 - 18 August 2022

Abstract

Feature selection plays an impactful role in deep recommender systems, which selects a subset of the most predictive features, so as to boost the recommendation performance and accelerate model optimization. The majority of existing feature selection methods, however, aim to select only a fixed subset of features. This setting cannot fit the dynamic and complex environments of practical recommender systems, where the contribution of a specific feature varies significantly across user-item interactions. In this paper, we propose an adaptive feature selection framework, AdaFS, for deep recommender systems. To be specific, we develop a novel controller network to automatically select the most relevant features from the whole feature space, which fits the dynamic recommendation environment better. Besides, different from classic feature selection approaches, the proposed controller can adaptively score each example of user-item interactions, and identify the most informative features correspondingly for subsequent recommendation tasks. We conduct extensive experiments based on two public benchmark datasets from a real-world recommender system. Experimental results demonstrate the effectiveness of AdaFS, and its excellent transferability to the most popular deep recommendation models.

Research Area(s)

  • automl, feature selection, recommender system

Bibliographic Note

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

Citation Format(s)

AdaFS: Adaptive Feature Selection in Deep Recommender System. / Lin, Weilin; Zhao, Xiangyu; Wang, Yejing et al.
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2022. p. 3309–3317 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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