Article

Diversifying Product Review Rankings: Getting the Full Picture

Authors:

Nima DokoohakiAuthors Info & Claims

WI-IAT '11: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Pages 138 - 145

https://doi.org/10.1109/WI-IAT.2011.33

Published: 22 August 2011 Publication History

Abstract

E-commerce Web sites owe much of their popularity to consumer reviews provided together with product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to build confidence in products they are planning to buy. At the same time, popular products have thousands of user-generated reviews. Current approaches to present them to the user or recommend an individual review for a product are based on the helpfulness or usefulness of each review. In this paper we look at the top-k reviews in a ranking to give a good summary to the user with each review complementing the others. To this end we use Latent Dirichlet Allocation to detect latent topics within reviews and make use of the assigned star rating for the product as an indicator of the polarity expressed towards the product and the latent topics within the review. We present a framework to cover different ranking strategies based on theuser's need: Summarizing all reviews, focus on a particular latent topic, or focus on positive, negative or neutral aspects. We evaluated the system using manually annotated review data from a commercial review Web site.

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Cited By

Huang CJiang WWu JWang G(2020)Personalized Review Recommendation based on Users’ Aspect SentimentACM Transactions on Internet Technology10.1145/341484120:4(1-26)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1145/3414841
Harel SAlbo SAgichtein ERadinsky K(2019)Learning Novelty-Aware Ranking of Answers to Complex QuestionsThe World Wide Web Conference10.1145/3308558.3313457(2799-2805)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313457
Vo CDuong DNguyen DCao T(2018)From Helpfulness Prediction to Helpful Review Retrieval for Online Product ReviewsProceedings of the 9th International Symposium on Information and Communication Technology10.1145/3287921.3287931(38-45)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3287921.3287931
Show More Cited By

Index Terms

Diversifying Product Review Rankings: Getting the Full Picture
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems

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cover image ACM Conferences

WI-IAT '11: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

August 2011

520 pages

ISBN:9780769545134

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

IEEE Computer Society

United States

Publication History

Published: 22 August 2011

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Cited By

Huang CJiang WWu JWang G(2020)Personalized Review Recommendation based on Users’ Aspect SentimentACM Transactions on Internet Technology10.1145/341484120:4(1-26)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1145/3414841
Harel SAlbo SAgichtein ERadinsky K(2019)Learning Novelty-Aware Ranking of Answers to Complex QuestionsThe World Wide Web Conference10.1145/3308558.3313457(2799-2805)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313457
Vo CDuong DNguyen DCao T(2018)From Helpfulness Prediction to Helpful Review Retrieval for Online Product ReviewsProceedings of the 9th International Symposium on Information and Communication Technology10.1145/3287921.3287931(38-45)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3287921.3287931
Al-Dhelaan MAl-Suhaim ASheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)Sentiment diversification for short review summarizationProceedings of the International Conference on Web Intelligence10.1145/3106426.3106525(723-729)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106525
Omari ACarmel DRokhlenko OSzpektor IPerego RSebastiani FAslam JRuthven IZobel J(2016)Novelty based Ranking of Human Answers for Community QuestionsProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911506(215-224)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911506
Santos RMacdonald COunis I(2015)Search Result DiversificationFoundations and Trends in Information Retrieval10.1561/15000000409:1(1-90)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1561/1500000040
Borin LForsberg MHeppin KJohansson RKjellandsson AIde NXia F(2012)Search result diversification methods to assist lexicographersProceedings of the Sixth Linguistic Annotation Workshop10.5555/2392747.2392764(113-117)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2392747.2392764
Brown B(2012)Beyond RecommendationsACM Transactions on Computer-Human Interaction10.1145/2395131.239513419:4(1-24)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1145/2395131.2395134
Dokoohaki NMatskin M(2012)Mining Divergent Opinion Trust Networks through Latent Dirichlet AllocationProceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)10.1109/ASONAM.2012.158(879-886)Online publication date: 26-Aug-2012
https://dl.acm.org/doi/10.1109/ASONAM.2012.158

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