MF-NCG: Recommendation Algorithm Using Matrix Factorization-based Normalized Cumulative Genre
International journal of intelligent engineering and systems · 2024
Collaborative filtering has emerged as one of the most prevalent techniques for various commercial recommendations.Utilizing a similarity measure to identify similar neighbors is essential to collaborative filtering.Behavior scores and user ratings have recently become increasingly important factors in determining similarity.However, the added users' behavior scores generate a more complex computation.This research proposes a new similarity technique incorporating the matrix factorization and users' behavior score-based similarity to minimize computation time.The matrix factorization technique uses singular value decomposition (SVD), and the users' behavior score-based similarity employs normalized cumulative genre (NCG).Compared to the previous algorithm (i.e., users' scores probability-based collaborative filtering), the experimental findings with the MovieLens 1M and 100k datasets demonstrated a faster computing time.In addition, with these datasets, our similarity reduces the root mean square error (RMSE) by 8.14% and 11.99% and the mean absolute error (MAE) by 13.52% and 15.81%.