A Comparison of Fuzzy Approaches to E-Commerce Review Rating Prediction
Giovanni Acampora, Georgina Cosma
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.173How to use a DOI?
- Review rating prediction, rating inference problem, multi-pint scale prediction, Fuzzy logic, Simplified Fuzzy ARTMAP, Fuzzy C-Means, ANFIS.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.173 ID - Acampora2015/06 ER -