EVALUATION OF ACCURACY BETWEEN ITEM --BASED AND D MATRIX FACTORIZATION RECOMMEND

Abstract

Recommender systems are used by e-commerce websites and streaming services to predict user opinion about products. This work examined two specific recommender algorithms, matrix factorization collaborative filtering algorithm and Item-based collaborative filtering, which utilizes item similarity. This study is to compare the prediction accuracy of the algorithms using Mean Square Error and Root Mean Square Error criteria. The work yielded a result which indicated that the matrix factorization collaborative filtering algorithm is more accurate than the Item-based collaborative filtering algorithm. From the study, the results of evaluation metrics showed that matrix factorization method having RMSE of 0.916250 and MAE 0.708731 scales slightly better than the item-based method having RMSE and MAE of 0.937089 and 0.719434. The study concluded that the matrix factorization method is more accurate than the item-based method when evaluating their prediction accuracy using RMSE and MAE.