Abstract
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 α-β-divergence-generalized model that enjoys fast convergence. Its ideas are three-fold: 1) generalizing its learning objective with α -β -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.
Original language | English (US) |
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Journal | IEEE Transactions on Cybernetics |
DOIs | |
State | Accepted/In press - 2021 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- α -β -divergence
- big data
- Computational modeling
- Convergence
- convergence analysis
- Data models
- Euclidean distance
- high-dimensional and sparse (HiDS) data
- Linear programming
- machine learning
- missing data estimation
- momentum
- non-negative latent factor analysis (NLFA)
- Predictive models
- recommender system (RS)
- Sparse matrices