MF-NCG: Recommendation Algorithm Using Matrix Factorization-based Normalized Cumulative Genre

Abstract

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%.