Title: Online products recommendation system using genetic kernel fuzzy C-means and probabilistic neural network

Authors: E. Manohar; D. Shalini Punithavathani

Addresses: Anna University, Chennai, India ' Anna University, Chennai, India

Abstract: The purchaser's review plays a significant role in choosing the purchasing activities for online shopping as a customer desires to obtain the opinion of other purchasers by observing their opinion through online products. However, most appropriate product selection from the best website is a challenging problem for online users. Accordingly, this paper proposes a hybrid recommendation system for identifying customer preferences and recommending the most appropriate product. To do this, first the dataset is collected and prepared in the pre-processing step. Genetic kernel fuzzy C-means (GAKFCM) is used for usage cluster formation after the pre-processing step. The different features are extracted from each cluster-based user interest level. The user interest levels are used as features for classifier to extract user knowledge discovery. Based upon the user interest level, the product recommendation is done using probabilistic neural network (PNN). The simulation results show high precision rate which clearly indicates that the proposed method is very useful and appealing.

Keywords: website; web-log; ranking; rating; review; products; genetic kernel fuzzy C-means; GAKFCM; probabilistic neural network; PNN.

DOI: 10.1504/IJBIDM.2020.108029

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.1, pp.32 - 53

Received: 01 Aug 2017
Accepted: 22 Nov 2017

Published online: 05 Apr 2020 *

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