A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Abstract

Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item amongst the potentially overwhelming set of alternative products or services. This work focused on using users' bank statements that explicitly shows inflow and outflow of funds. The dataset used is real and reliable because the use of non-reliable data in a recommender system causes users lack of trust in the system. However, the data collected were anonymized for privacy reasons. The recommender system was developed as a web application using Java programming language. Unlike other recommender systems, the graph-oriented database management system was used. In Google news, 38% of the total views are the result of recommendations; similarly, 60% of the rented movies from Netflix come from recommendations and more than that Amazon sales percentage due to recommendations are 35%. Successful integration of recommendation system by online companies like Amazon, eBay, Flipkart amongst others impelled the research community to avail similar benefits in Financial domain to recommend product and services [26]. Therefore, recommendation systems are considered an expedient factor in business nowadays. The aim of all recommender systems is to provide recommendation that will be favourably evaluated and accepted by its users.