Recommender System: Towards Classification of Human Intentions in E-Shopping Using Machine Learning

Abstract

Recommender systems were introduced in mid-1990 for assisting the users to choose a correct product from innumerable choices available. The basic concept of a recommender system is to advise a new item or product to the users instead of the manual search, because when user wants to buy a new item, he is confused about which item will suit him better and meet the intended requirements. From google news to netflix and from Instagram to LinkedIn, recommender systems have spread their roots in almost every application domain possible. Now a days, lots of recommender system are available for every field. In this paper, overview of recommender system, recommender approaches, application areas and the challenges of recommender system, is given. Further, we study conduct an experiment on online shoppers’ intention to predict the behavior of shoppers using Machine learning algorithms. Based on the results, it is observed that Random forest algorithm performs the best with 93% ROC value.