AutoField : Automating Feature Selection in Deep Recommender Systems

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

25 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationWWW '22
Subtitle of host publicationProceedings of the ACM Web Conference 2022
EditorsFrédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, Lionel Médini
PublisherAssociation for Computing Machinery
Pages1977-1986
Number of pages10
ISBN (Print)978-1-4503-9096-5
Publication statusPublished - Apr 2022

Publication series

NameWWW - Proceedings of the ACM Web Conference

Conference

Title31st ACM World Wide Web Conference, WWW 2022
PlaceFrance
CityVirtual, Online
Period25 - 29 April 2022

Abstract

Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most existing deep recommender systems, however, focus on designing sophisticated neural networks, while neglecting the feature selection process. Typically, they just feed all possible features into their proposed deep architectures, or select important features manually by human experts. The former leads to non-trivial embedding parameters and extra inference time, while the latter requires plenty of expert knowledge and human labor effort. In this work, we propose an AutoML framework that can adaptively select the essential feature fields in an automatic manner. Specifically, we first design a differentiable controller network, which is capable of automatically adjusting the probability of selecting a particular feature field; then, only selected feature fields are utilized to retrain the deep recommendation model. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our framework. We conduct further experiments to investigate its properties, including the transferability, key components, and parameter sensitivity.

Research Area(s)

  • AutoML, Feature Selection, Recommender System

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

AutoField: Automating Feature Selection in Deep Recommender Systems. / Wang, Yejing; Zhao, Xiangyu; Xu, Tong et al.
WWW '22: Proceedings of the ACM Web Conference 2022. ed. / Frédérique Laforest; Raphaël Troncy; Elena Simperl; Deepak Agarwal; Aristides Gionis; Ivan Herman; Lionel Médini. Association for Computing Machinery, 2022. p. 1977-1986 (WWW - Proceedings of the ACM Web Conference).

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