Non-Negative Latent Factor Model Based on β-Divergence for Recommender Systems

Document Type

Article

Publication Date

8-1-2021

Abstract

Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF models mostly adopt Euclidean distance in their objective function, which represents a special case of a {{β }} -divergence function. Hence, it is highly desired to design a {{β }} -divergence-based NLF ( {{β }} -NLF) model that uses a {{β }} -divergence function, and investigate its performance in recommender systems as {{β }} varies. To do so, we first model {{β }} -NLF's learning objective with a {{β }} -divergence function. Subsequently, we deduce a general single latent factor-dependent, non-negative and multiplicative update scheme for {{β }} -NLF, and then design an efficient {{β }} -NLF algorithm. The experimental results on HiDS matrices from industrial applications indicate that by carefully choosing the value of {{β }} , {{β }} -NLF outperforms an NLF model with Euclidean distance in terms of accuracy for missing data prediction without increasing computational time. The research outcomes show the necessity of using an optimal {{β }} -divergence function in order to achieve the best performance of an NLF model on HiDS matrices. Hence, the proposed model has both theoretical and application significance.

Identifier

85110447998 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Systems

External Full Text Location

https://doi.org/10.1109/TSMC.2019.2931468

e-ISSN

21682232

ISSN

21682216

First Page

4612

Last Page

4623

Issue

8

Volume

51

Grant

cstc2017kjrc-cxcytd0149

Fund Ref

National Natural Science Foundation of China

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