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Detecting Anomalous Ratings Using Matrix Factorization for Recommender Systems Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Learning User Credibility on Aspects from Review Texts

Authors : Yifan Gao, Yuming Li, Yanhong Pan, Jiali Mao, Rong Zhang

Published in: Web-Age Information Management

Publisher: Springer International Publishing

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Abstract

Spammer detection has been popularly studied these years which aims at filtering unfair or incredible customers. Most users have different backgrounds or preferences so that they make distinct reviews/ratings, however they can not be treated as spammers. To date, the existing previous spammer detection technology has limited usability. In this paper, we propose a method to calculate user credibility on multi-dimensions by considering users difference related to their personalities e.g. background and preference. Firstly, we propose to evaluate customer credibilities on aspects with the consideration of different concerns given by different customers. A boot-strapping algorithm is applied to detect the intrinsic aspects of review text and the aspect ratings are assigned by mining semantic polarity. Then, an iteration algorithm is designed for estimating credibilities by considering the consistency between individual ratings and overall ratings on aspects. Finally, experiments on the real dataset demonstrate that our method outperforms baseline systems.

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Footnotes
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Metadata
Title
Learning User Credibility on Aspects from Review Texts
Authors
Yifan Gao
Yuming Li
Yanhong Pan
Jiali Mao
Rong Zhang
Copyright Year
2016
Book
Web-Age Information Management

Print ISBN: 978-3-319-39957-7

Electronic ISBN: 978-3-319-39958-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39958-4_7