Archetypal Personalized Recommender System for Mobile Phone Users

The process of mobile phone selection, for several reasons, depends on a number of common individual features possessed by the manufacturers. The recent advance in these products’ functionalities is identified as a key factor for the growing number of brands and models that compete in its fierce market and thus leads to the problem of product selection. Product comparisons, as a result, are becoming more difficult thus favoring the use of computer-based decision systems to assist consumers in scouting for information on mobile products that can best satisfy their needs. This study proposes an archetypal personalized recommender system that can intelligently mine information about the features of mobile phones and provides professional services to potential buyers. Consumer preferences and product features are technically expressed with the aid of Triangular Fuzzy Numbers while Fuzzy Near Compactness is employed to measure the feature-need similarities in order to recommend optimal products that best satisfy the needs. Finally, an experimental study is performed to examine the feasibility and effectiveness of the proposed system.