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

Document Type

Article

Publication Date

8-1-2022

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.

Identifier

85101748119 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2020.3026425

e-ISSN

21682275

ISSN

21682267

PubMed ID

33600329

First Page

8006

Last Page

8018

Issue

8

Volume

52

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