Proceedings of the 1st International Conference on Information Technologies in Education and Learning

An Approach for Reducing the Numeric Rating Bias

Authors
Xiu Li, Huimin Wang, Jian Xu
Corresponding Author
Xiu Li
Available Online March 2016.
DOI
https://doi.org/10.2991/icitel-15.2016.11How to use a DOI?
Keywords
bias; evaluation; review mining; e-business
Abstract

There is bias in customer reviews and the associated ratings. We propose a method to identify and reduce such bias on the part of reviewers. There are three phases in our approach. Firstly, we conduct Rating-aware sentiment analysis for each review text accompanied with the numeric ratings. Then, we extract features of review text and do manual labelling for training learning machine. At last, SVM is used to train the learner to reduce the rating bias. We applied our method on the dataset obtained from amazon.com. Results suggest that biased ratings have effects on customer ratings significantly in recommender systems and that this bias can be substantially reduced by our model.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 1st International Conference on Information Technologies in Education and Learning
Series
Advances in Computer Science Research
Publication Date
March 2016
ISBN
978-94-6252-168-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/icitel-15.2016.11How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiu Li
AU  - Huimin Wang
AU  - Jian Xu
PY  - 2016/03
DA  - 2016/03
TI  - An Approach for Reducing the Numeric Rating Bias
BT  - Proceedings of the 1st International Conference on Information Technologies in Education and Learning
PB  - Atlantis Press
SP  - 48
EP  - 53
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitel-15.2016.11
DO  - https://doi.org/10.2991/icitel-15.2016.11
ID  - Li2016/03
ER  -