A hybrid recommendation system for E-commerce based on product description and user profile

E-commerce is an online trading system that eases transactions for both sellers and consumers without having to meet in person. The prevalence of e-commerce has increased competition amongst sellers, hence the users of e-commerce has to increase their performance, one of them by using recommendation system. This research develops a hybrid recommendation system for e-commerce that implements Content-based Filtering and Collaborative Filtering methods, which will compute the simmilarities of product description and user profile. In experiment results, it was found that the recommendation has similarity with product description and the preference of user profile with the average of precision value is 67.5% and recall value is 71.47%.