Collaborative filtering approach with decision tree technique

Abstract

Rapid advances in data collection and storage technology has enabled organizations (especially e-commerce) to accumulate vast amounts of data. The amount of data kept in computer files and databases is growing at a phenomenal rate because customers are evolving to use e- commerce services. So processing of large number of coustomer’s past purchase records is becoming a new challenge in e-commerce. The primary goal of e-commerce services is to build the systems where customers can get their likely recommended products relevant to their past purchase. We have implemented collaboratives filtering with supervised learning techniques. One of supervised learning techniques is Decision Tree. We have used Decision Tree to cluster similar type of customers according to active customer preferences (or tastes). In our new approach, a collaborative filtering based recommender system will recommended Top-k likely products according to customers preferences (or tastes) by considering past purchase record (or implicit ratings) of its clustered customers. This system will also recommend or predict Top-k likely products to particular customers by considering the cases when clustered customers have given explicit ratings (or votes) to their previously purchased products.