E – COMMERCE PRODUCT RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING

Abstract

Internet usage has experienced an exponential increase in the last ten years. This increase has providedopportunity for other enterprises that depend on it to develop and flourish as well. One such opportunity is Ecommerce. E-commerce is increasing at a very fast pace and with the rise of popularity in E-Commerce,Recommendation has become equally crucial as well. Recommendation is providing appropriate suggestions tothe user based on his/her interest and requirement. Recommendations to users can be provided on price, livingarea, wish listed items, cart items, searched items and previously purchased items. Recommendation systemsenhance user experience, increase sales and improve user's participation. Our suggested recommendation systemwill suggest products for both new users and regular users. This recommendation system applies model basedcollaborative filtering and recommends items based on rating and purchase history of previous old users. Evennew users will receive recommendations of new products, trending products and sale products. Existing userswill receive recommendations based on recently viewed items, complementary items, etc. As we areestablishing a new online shopping website, at first, there are no ratings by users for various products, and inthis situation, recommendations are generated from textual clustering analysis of product description. Modelbased Collaborative Filtering and Textual Clustering will assist us in high accuracy as well as targeting everycategory of users. E Commerce is becoming more popular and the recommendation system coupled with ECommerce is like icing on the cake