Semi-SGD: Semi-supervised Learning based Spammer Group Detection in Product Reviews

被引:5
|
作者
Zhang, Lu [1 ]
Yuan, Yang [2 ]
Wu, Zhiang [1 ]
Cao, Jie [1 ]
机构
[1] Nanjing Univ Finance & Econ, Jiangsu Prov Key Lab E Business, Nanjing, Jiangsu, Peoples R China
[2] Nuctech JiangSu Co Ltd, Changzhou, Jiangsu, Peoples R China
来源
2017 FIFTH INTERNATIONAL CONFERENCE ON ADVANCED CLOUD AND BIG DATA (CBD) | 2017年
基金
中国国家自然科学基金;
关键词
Spammer Group Detection; Semi-supervised Learning; Naive Bayes Classifier; EM Algorithm; Amazon.cn;
D O I
10.1109/CBD.2017.70
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The purchase decision of customers in e-commerce platforms is strongly influenced by product ratings and reviews. Driven by the profits, review spammers post fake reviews to promote their products or demote their competitors' products. Differ from individual spammers, the spammer groups manipulate reviews together and can be more damaging. Existing work for spammer group detection extract candidate groups from review data and identify the spammer groups using unsupervised spamicity ranking methods. However, the labeled and unlabeled data are existing simultaneously in practice and no method makes good use of both these data in spammer group detection. In this paper, we propose a semi-supervised learning based spammer group detection method (Semi-SGD), which trains a Naive Bayes classifier on a small set of labeled data as an initial classifier, and then incorporates unlabeled data with Expectation Maximization (EM) algorithm to improve the initial classifier iteratively. Experiments on Amazon.cn datasets show that our proposed Semi-SGD is efficient and effective.
引用
收藏
页码:368 / 373
页数:6
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