Recommender System: Personalizing User Experience or Scientifically Deceiving Users?

Abstract

Recommender system is taking the lead among many things that the digital world offers today, to every customer visiting online portals for any service. Since its popularity from the time of Netflix competition, recommender system has become more visible and an important marketing and sales tool for corporates augmenting their offers online. Ongoing research initiatives in recommender systems, large datasets available for users across the globe, and corporate collaborations have led to improvised algorithms, and reduced errors in estimating predictions. Software and hardware tools that enable easy gathering of implicit and explicit data have helped recommender system to quickly adapt to the needs of the users. It is in this background the possibility of recommender system inducing the customer to pre-determined items by presenting fabricated predictions, as if it is resultant of scientific principles, need to be considered. In this paper, we give an overview of the recommender system, discuss how various components of the recommender system may be manipulated to allure innocent customers with false ratings, and also discuss the importance of engaging stakeholders to develop a robust recommender system.