An Algorithm Model For Incremental Dectection of Spam Reviews

Surging smartphone use and pervasive O2O services mean customers can post their reviews about restaurants and shops online. However, many merchants may hire some people to post positive but fraud reviews in order to attract more customers. Therefore, a model need to be built to detect spam reviews....

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Bibliographic Details
Published inInternational journal of modeling and optimization Vol. 6; no. 1; pp. 45 - 48
Main Authors Wang, Maoan, Sun, Jun, Wu, Yifan, Wu, Guoshi
Format Journal Article
LanguageEnglish
Published 01.02.2016
Subjects
Algorithms
Batch processing
Classification
Construction
Customers
Fraud
Optimization
Spamming
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Summary:Surging smartphone use and pervasive O2O services mean customers can post their reviews about restaurants and shops online. However, many merchants may hire some people to post positive but fraud reviews in order to attract more customers. Therefore, a model need to be built to detect spam reviews. In this paper, firstly, we build a detection model using traditional batch processing which view the detection as a binary classification problem. Next, since many reviews are coming sequentially, batch processing is not efficient and useful. We will use another incremental algorithm-Hoeffding Option Tree to update the model without processing the past data repeatedly. We find that the incremental method can drastically improve the speed and the accuracy is also satisfying.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2010-3697
2010-3697
DOI:10.7763/IJMO.2016.V6.501