SISTEM REKOMENDASI PRODUK MENGGUNAKAN IMPLICIT FEEDBACK BERBASIS COLLABORATIVE FILTERING PADA E-COMMERCE - Muhammad Nugraha Mahardhika (2023)
Currently, the Indonesian people on buy and sell activities depend on e-commerce. The high growth of e-commerce produces transaction data on a massive scale can be used as a marketing strategy by companies, one of which is the Recommendation System. Recommendation System is a tool for estimate interested product based on matching the characteristics of each user with machine learning. Recommendation systems generally use collaborative filtering explicit feedback as a value of user interest on product. However, this causes data limitation problems (cold-start) because only based on transaction data that has been rated by the user. Instead of using explicit feedback, other solutions can use implicit feedback to avoid cold-start problems. By using implicit feedback, system can predict based on the number of user transactions for stores and product category. In this study, Singular Value Decomposition (SVD) is used as a matrix factorization model algorithm to find similarity between one and another user based on the feedback value. The results of the model show good performance with score RMSE ± 0,865 and MAE ± 0,508.