C2F: A Clustering Based Collaborative Filtering approach for recommending product to ecommerce user

The trust of sellers and transactions is a very important issue in e-commerce and e-service environments. Innovations in technology and faster increase of data sets have presided over today´s age of marketing. This resulted in need of adapting a mechanism which not only will analyze the customer behavior but also generate a good profitable amount to ecommerce industry. Keeping in view of this rapid engagement, we propose a Clustering Based Collaborative Filtering (C2F) approach for analyzing customer behavior and subsequently delivering accurate recommendation to the user. This approach mainly divided into two phases: clustering, and collaborative filtering to determine an accurate recommendation for customer. Adapting this technology will make an efficient way of proceeding the preexisting recommender system in terms of well suited use of C2F algorithm. An experimental evolution has been done while considering a sample data sets tabulated according to various characteristics.