A NOVEL COMBINED BUSINESS RECOMMENDER SYSTEM MODEL USING CUSTOMER INVESTMENT SERVICE FEEDBACK

Abstract

The aim of the study was to present a new business model of an investment recommender system using customer investment service feedback based on fuzzy neural inference solutions and customized investment services. The model designed to support the system’s process in investment companies. The type of research was qualitative and used of exploratory study and extensive library research. The model divided into two main parts using customer investment service feedback: data analysis and decision making. In this model, seven group factors proposed to implement the model of the proposed system of investment jobs through the potential investors. Machine learning use in this process and next ANFIS, which is an implementation of the neural art community uses the establishment of fuzzy logic judgment directly forward. The system act like a system consultant, studies the investor's past behavior and recommends relevant and accurate recommendations to the user for most appropriate investment.