A Comparison of Fuzzy Approaches to E-Commerce Review Rating Prediction

DOI

10.2991/ifsa-eusflat-15.2015.173How to use a DOI?

Keywords

Review rating prediction, rating inference problem, multi-pint scale prediction, Fuzzy logic, Simplified Fuzzy ARTMAP, Fuzzy C-Means, ANFIS.

Abstract

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor.

Copyright

© 2015, 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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TY  - CONF
AU  - Giovanni Acampora
AU  - Georgina Cosma
PY  - 2015/06
DA  - 2015/06
TI  - A Comparison of Fuzzy Approaches to E-Commerce Review Rating Prediction
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 1223
EP  - 1230
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.173
DO  - 10.2991/ifsa-eusflat-15.2015.173
ID  - Acampora2015/06
ER  -