期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 8, 页码 8006-8018出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3026425
关键词
Computational modeling; Sparse matrices; Convergence; Data models; Predictive models; Linear programming; Euclidean distance; -divergence; big data; convergence analysis; high-dimensional and sparse (HiDS) data; momentum; machine learning; missing data estimation; non-negative latent factor analysis (NLFA); recommender system (RS)
类别
资金
- National Natural Science Foundation of China [62002337, 61772493, 61802360]
- Natural Science Foundation of Chongqing (China) [cstc2019jcyjjqX0013]
- Pioneer Hundred Talents Program of Chinese Academy of Sciences
To quantify user-item preferences, a study proposed an alpha-beta-divergence-generalized model, which achieves a more accurate representation and faster convergence of HiDS data through multidimensional learning and parameter adjustment.
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.
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