Development of E-Commerce Website Recommender System Using Collaborative Filtering and Deep Learning Techniques

Abstract

Recommender system or recommendation system is becoming an increasingly important technology on e-commerce websites to help users find products that suit their preferences. However, the growing number of users and products makes finding the right product difficult. Therefore, this study aims to develop a recommender system on e-commerce websites using collaborative filtering and deep learning techniques. Collaborative filtering is used to find similarities between users based on their preferences, while deep learning is used to improve the performance of the recommender system in generating more accurate recommendations. The test method is carried out by comparing the performance of the recommender system developed with the recommender system that already exists on the e-commerce website. The results of the test show that the recommender system developed is able to provide recommendations that are more accurate and more in line with user preferences compared to the existing recommender system.