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An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences
Shang, Mingsheng1,2; Yuan, Ye1,2,3; Luo, Xin1,2,4; Zhou, MengChu5,6,7
2021-02-17
摘要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.
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)
DOI10.1109/TCYB.2020.3026425
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
页码13
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000732284400001
语种英语