AutoDenoise : Automatic Data Instance Denoising for Recommendations

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

5 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationWWW '23: Proceedings of the ACM Web Conference 2023
EditorsYing Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo, Geert-Jan Houben
PublisherAssociation for Computing Machinery, Inc
Pages1003-1011
Number of pages9
ISBN (Electronic)9781450394161
Publication statusPublished - Apr 2023

Publication series

NameACM Web Conference - Proceedings of the World Wide Web Conference, WWW

Conference

TitleACM Web Conference 2023 (WWW '23)
LocationHybrid
PlaceUnited States
CityAustin
Period30 April - 4 May 2023

Abstract

Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests. While a large number of denoising studies are emerging in the recommender system community, all of them suffer from highly dynamic data distributions. In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems. To be specific, AutoDenoise serves as an agent in DRL to adaptively select noise-free and predictive data instances, which can then be utilized directly in training representative recommendation models. In addition, we design an alternate two-phase optimization strategy to train and validate the AutoDenoise properly. In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability. We conduct extensive experiments to validate the effectiveness of AutoDenoise combined with multiple representative recommender system models. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Research Area(s)

  • Instance Denoising, Recommender System, Reinforcement learning

Bibliographic Note

Publisher Copyright: © 2023 ACM.

Citation Format(s)

AutoDenoise: Automatic Data Instance Denoising for Recommendations. / Lin, Weilin; Zhao, Xiangyu; Wang, Yejing et al.
WWW '23: Proceedings of the ACM Web Conference 2023. ed. / Ying Ding; Jie Tang; Juan Sequeda; Lora Aroyo; Carlos Castillo; Geert-Jan Houben. Association for Computing Machinery, Inc, 2023. p. 1003-1011 (ACM Web Conference - Proceedings of the World Wide Web Conference, WWW).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review