Accelerated - Generic gradient descent for e-commerce recommender systems

Abstract

The online product reviews play an important role in the field of e-commerce as rating of the product infers the preference for customers and they rely on them while making purchases. The main aim of providing an efficient recommender system is to gain useful information for user based on quality, worth and quantity of the product. The reviews given by the customers are used to extract the features of products that evaluate the similarity content reviewed by another customer. Therefore, the reviews written by the distinct persons would be similar, but with different words of expression shows difficulty during review differentiation. The developed model performed mining customer review for a set of manufactured goods and their characteristics are used as product features. The proposed Gated Recurrent Unit with Accelerated-Generic Gradient Descent (GRU-AGGD) technique extracts the relevant and sufficient features from the review to overcome the existing optimization problems. These relevant features are the summary of the product used to predict the rank using the users-attributes and users-items matrix and these two matrixes give rise to Non-Negative Matrix Factorization model to provide recommendations to user for the product. The experimental results of product recommender system are evaluated using the proposed GRU-AGGD that shows the improvement in terms of performance measures. The average MSE and RMSE values obtained for the proposed GRU-AGGD technique are 0.84 and 0.91 respectively, which are higher when compared to the Bayesian recommender models.