Attract Human Loyalty: Revealing Innovative Recommender System using Nebulous and Intelligent Techniques in Virtual Business Realm

Abstract

Lately, third-party businesses have been offering a variety of Internet apps due to innovations in digital technologies. One technological innovation whose use has gained importance is the Internet of Things (IoT) which touches on several facets of everyday living. Due to its ability to track and surveillance human behavior through smart devices. As a result, this technology has data from gathering smart devices data on people and their preferences. Accordingly, it has become easy to predict people's needs and tendencies and recommend them. Herein, we are leveraging IoT applications in the recommender system (RS). Wherein, one of the key components of efficient IoT-based smart commerce systems is locating products and services that consumers would find interesting and persuading them to purchase them. Hence, harnessing effective IoT application-based RS is a crucial matter. This matter is a catalyst for constructing a robust intelligent decision-support model for selecting optimal RS-IoT for serving human needs. We are leveraging multi-criteria decision making (MCDM) techniques such as MEthod based on the Removal Effects of Criteria (MEREC) Method to determine the weights of criteria utilized in Multi-Attributive Border Approximation Area Comparison (MABAC) to evaluate and rank a set of RSs-IoT. These alternatives have been evaluated by using triangular fuzzy number (TFN) with its scale.