A Recommender System for the Optimal Combo Offers with Cost Benefit Analysis

In today’s digital world, enterprises have the need to recommend their products to customers, in easy way that is without consuming much time. As a result, e-commerce websites have recently started tracking user data automatically while users surf the web for different purposes. The collected large-scale data need to be used effectively in diversified recommender system applications. The main part of e-commerce, which is useful to both customers and businesses is the recommendation system. This research study has used K-Nearest Neighbor algorithm for enabling item-based collaborative filtering to recommend products. This study has also used Cost Benefit Analysis (CBA) for recommending optimal combo offers. The investigational evaluations showed the efficiency of proposed multi-recommendation models.