Non-Negative Matrix Factorization Based Recommender System using Female Daily Implicit Feedback - Hani Nurrahmi; Agung Toto Wibowo; Selly Meliana (School of Computing, Telkom University, 2022)
Recommender Systems is widely used by e-commerce to provide recommendations of products that are probably to be the interest to users. One of the recommender system algorithms that can be implemented is Non-negative Matrix Factorization (NMF) which receives explicit feedback in the form of user ratings. Although this method is effective, there are problems faced by explicit feedback as input, e.g. there are users who act as grey-sheep or black-sheep by providing dishonest ratings as explicit feedback. On the opposite, dishonest feedback least frequently occurs in implicit feedback. Therefore, in this study, we used implicit feedback to recommend products by taking the implicit feedback obtained from Female Daily’s mobile application as a case study. There are three types of implicit feedback: View Product Detail, View Review Detail, and Add to Wishlist. We experimented with the NMF algorithm provided by Surprise library using two implicit ratings weighting scenarios: accumulative weighting and maximum weighting. We combined several NMF parameters and run our experiment in 5-fold cross-validation. The best performance result in accumulative weighting is MSE = 1,2969, RMSE = 1,1388, MAE = 0,7909. Meanwhile, the best performance result in maximum weighting is MSE = 0,6742, RMSE = 0,8211, MAE = 0,5924.