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

Mingsheng Shang, Ye Yuan, Xin Luo, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
JournalIEEE Transactions on Cybernetics
DOIs
StateAccepted/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

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