short-paper

A Reinforced Semi-supervised Neural Network for Helpful Review Identification

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Ping LiAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 2021 - 2024

https://doi.org/10.1145/3340531.3412101

Published: 19 October 2020 Publication History

Abstract

It is crucial to recommend helpful product reviews to consumers in e-commercial service, as the helpful ones can promote consumption. Existing methods for identifying helpful reviews are based on the supervised learning paradigm. The capacity of supervised methods, however, is limited by the lack of annotated reviews. In addition, there is a serious distributional bias between the labeled and unlabeled reviews. Therefore, this paper proposes a reinforced semi-supervised neural learning method (abbreviated as RSSNL) for helpful review identification, which can automatically select high-related unlabeled reviews to help training. Concretely, RSSNL composes with a reinforced unlabeled review selection policy and a semi-supervised pseudo-labeling review classifier. These two parts train jointly and integrate together based on the policy gradient framework. Extensive experiments on Amazon product reviews verify the effectiveness of RSSNL for using unlabeled reviews.

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It is crucial to recommend helpful product reviews to consumers in e-commercial service, as the helpful ones can promote consumption. Existing methods for identifying helpful reviews are based on the supervised learning paradigm. The capacity of supervised methods, however, is limited by the lack of annotated reviews. In addition, there is a serious distributional bias between the labeled and unlabeled reviews. Therefore, this paper proposes a reinforced semi-supervised neural learning method (abbreviated as RSSNL) for helpful review identification, which can automatically select high-related unlabeled reviews to help training. Concretely, RSSNL composes with a reinforced unlabeled review selection policy and a semi-supervised pseudo-labeling review classifier. These two parts train jointly and integrate together based on the policy gradient framework. Extensive experiments on Amazon product reviews verify the effectiveness of RSSNL for using unlabeled reviews.

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References

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Cited By

Taha K(2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102178
Feng YRen ZZhao WSun MLi PDiaz FShah CSuel TCastells PJones RSakai T(2021)Multi-Type Textual Reasoning for Product-Aware Answer GenerationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462899(1135-1145)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462899

Index Terms

A Reinforced Semi-supervised Neural Network for Helpful Review Identification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Semi-supervised learning

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Published: 19 October 2020

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

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Cited By

Taha K(2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102178
Feng YRen ZZhao WSun MLi PDiaz FShah CSuel TCastells PJones RSakai T(2021)Multi-Type Textual Reasoning for Product-Aware Answer GenerationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462899(1135-1145)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462899

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