Building a Recommendation System for Online Shopping Based on Item-Based Collaborative Filtering

Abstract

This research applied an innovation in developing online shopping, using recommendation system. Recommendation System applies finding knowledge technique which is called item-based Collaborative Filtering. This works with by building information about items that are preferred by the customers. Collaborative Filtering filters data based on similarities or certain characteristics, so that the system is able to provide information based on patterns from a certain group of data that are almost the same. With recommendation system, customers could benefit from the recommended items which they may favour, generated automatically by the system. It is hoped that it could improve the convenience to shop and reduce the time needed by customers to search for items. Therefore it could increase the competitiveness of online shops that use a recommendation system.