research-article

From Helpfulness Prediction to Helpful Review Retrieval for Online Product Reviews

Authors:

Tru CaoAuthors Info & Claims

SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

Pages 38 - 45

https://doi.org/10.1145/3287921.3287931

Published: 06 December 2018 Publication History

Abstract

Nowadays, online product reviews belong to a valuable data source for customers in e-commerce. They provide customers with helpful details about a given product before customers make a decision on purchasing that product. Nevertheless, in this regard, if the e-commerce system returns too many reviews to customers and the reviews are not well presented in a relevant manner, the reviews might become cumbersome and time-consuming. In this paper, we define a helpful review retrieval task to support the customers by returning a ranked list of helpful reviews according to their helpfulness about the product of their interest. For an effective solution to the task, we also propose a method with an enhanced list of features for review representation and a multiple linear regression model using the elastic net regularization method. Our method is comprehensive as examining the task in its entirety from review's helpfulness prediction to helpful review retrieval for online product reviews. Evaluated on a real world Amazon dataset of the reviews about electronic devices, our method outperforms the others with the best values: 0.8 for the Normalized Discounted Cumulative Gain measure and 0.83 for the Accuracy measure. Such promising experimental results confirm the effectiveness of our method for the task.

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Cited By

Sharma SSingh LTiwari R(2024)Design of an Efficient Integrated Feature Engineering based Deep Learning Model Using CNN for Customer’s Review Helpfulness PredictionWireless Personal Communications10.1007/s11277-023-10834-1133:4(2125-2161)Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11277-023-10834-1
Saptono RMine T(2022)Adaptive Neighborhood Distribution-based Model for Estimating Helpful Votes of Customer Review2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00029(143-150)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00029
Saptono RMine T(2020)Time-based Sampling Methods for Detecting Helpful Reviews2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00076(508-513)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00076
Show More Cited By

Index Terms

From Helpfulness Prediction to Helpful Review Retrieval for Online Product Reviews
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
      2. Retrieval effectiveness
    2. Retrieval models and ranking
      1. Learning to rank

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SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

December 2018

496 pages

ISBN:9781450365390

DOI:10.1145/3287921

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SOICT: School of Information and Communication Technology - HUST
NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2018

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SoICT 2018

SoICT 2018: The Ninth International Symposium on Information and Communication Technology

December 6 - 7, 2018

Danang City, Viet Nam

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

Sharma SSingh LTiwari R(2024)Design of an Efficient Integrated Feature Engineering based Deep Learning Model Using CNN for Customer’s Review Helpfulness PredictionWireless Personal Communications10.1007/s11277-023-10834-1133:4(2125-2161)Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11277-023-10834-1
Saptono RMine T(2022)Adaptive Neighborhood Distribution-based Model for Estimating Helpful Votes of Customer Review2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00029(143-150)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00029
Saptono RMine T(2020)Time-based Sampling Methods for Detecting Helpful Reviews2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00076(508-513)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00076
Bilal MMarjani MLali MMalik NGani AHashem I(2020)Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”IEEE Access10.1109/ACCESS.2020.29894638(77227-77244)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2989463
Du JRong JMichalska SWang HZhang Y(2019)Feature selection for helpfulness prediction of online product reviews: An empirical studyPLOS ONE10.1371/journal.pone.022690214:12(e0226902)Online publication date: 23-Dec-2019
https://doi.org/10.1371/journal.pone.0226902
Bilal MMarjani MHashem IAbdullahi ATayyab MGani A(2019)Predicting Helpfulness of Crowd-Sourced Reviews: A Survey2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS48846.2019.9024814(1-8)Online publication date: Dec-2019
https://doi.org/10.1109/MACS48846.2019.9024814

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