@inproceedings{b74c5deed0a141d79e8a47cf8c76ff42,
title = "Learning user credibility on aspects from review texts",
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 multidimensions 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.",
author = "Yifan Gao and Yuming Li and Yanhong Pan and Jiali Mao and Rong Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 17th International Conference on Web-Age Information Management, WAIM 2016 ; Conference date: 03-06-2016 Through 05-06-2016",
year = "2016",
doi = "10.1007/978-3-319-39958-4\_7",
language = "英语",
isbn = "9783319399577",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "78--91",
editor = "Bin Cui and Xiang Lian and Dexi Liu and Nan Zhang and Jianliang Xu",
booktitle = "Web-Age Information Management - 17th International Conference, WAIM 2016, Proceedings",
address = "德国",
}