An α-β-Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences

• Published in: IEEE transactions on cybernetics 2022-08, Vol.52 (8), p.8006-8018
• Main Authors: Shang, Mingsheng, Yuan, Ye, Luo, Xin, Zhou, MengChu
• Format: Article
• Language: English
• Subjects: Algorithms; big data; Computational modeling; Convergence; convergence analysis; Data models; Empirical analysis; Euclidean distance; Factor analysis; high-dimensional and sparse (HiDS) data; Learning; Linear programming; machine learning; Missing data; missing data estimation; momentum; non-negative latent factor analysis (NLFA); Predictive models; recommender system (RS); Recommender systems; Sparse matrices; α–β-divergence
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2020.3026425

To quantify user-item preferences, a recommender system (RS) commonly adopts a high-dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative latent factor analysis model relying on a single latent factor (LF)-dependent, non-negative, and multiplicative update algorithm. However, existing models' representative abilities are limited due to their specialized learning objective. To address this issue, this study proposes an \alpha - \beta -divergence-generalized model that enjoys fast convergence. Its ideas are three-fold: 1) generalizing its learning objective with \alpha - \beta -divergence to achieve highly accurate representation of HiDS data; 2) incorporating a generalized momentum method into parameter learning for fast convergence; and 3) implementing self-adaptation of controllable hyperparameters for excellent practicability. Empirical studies on six HiDS matrices from real RSs demonstrate that compared with state-of-the-art LF models, the proposed one achieves significant accuracy and efficiency gain to estimate huge missing data in an HiDS matrix.