Ep-Satty-MCDM based business decision-making model using behaviour and review data

Abstract

Reaching the maximum number of customers at the right time to increase sales and profitability of the business is the primary goal of Electronic Commerce (E-Commerce). However, owing to the low-influenced product, the profitability of e-commerce has been drastically affected in recent years. Therefore, this work proposes an Empirical Probability assigned Satty’s method integrated Multicriteria Decision-Making model (EP-Satty-MCDM)-based business decision-making model for improving the sales of low-influenced products by advertising with celebrities. Primarily, the authenticated user securely downloaded the encrypted data using Armstrong number private key generated-Trident Curve-Cryptography (Arm-TCC) in the web application. After that, the data is cleansed and the attributes of review and behavior data are extracted. Then, by utilizing the Interval-valued Atanassov intuitionistic fuzzy-based Mann-Whitney U test (IAF-MWU), the correlation between the review and behavior data is evaluated. The correlated features under each user are mapped under the product, and semantic ontology is constructed, where the data is again mapped with the product’s subsections. Afterward, the domains are extracted. Thereafter, to identify whether the product is high-influenced or low-influenced, the obtained ratings from ontology and extracted domains are inputted into the Boosting Regression Tree-Recurrent Neural Network (BRT-RNN). Then, for the decision-making, the positively forecasted celebrities with their garment and low-influenced products are given as the input to EP-Satty-MCDM. The experimental outcomes exhibited that the proposed technique withstands maximum accuracy when contrasted with the existing methodologies.