Incorporating side information into Robust Matrix Factorization with Bayesian Quantile Regression

Andrey Babkin

Statistics & Probability Letters, 2020, vol. 165, issue C

Abstract: Matrix Factorization is a widely used technique for modeling pairwise and matrix-like data. It is frequently used in pattern recognition, topic analysis and other areas. Side information is often available, however utilization of this additional information is problematic in the pure matrix factorization framework. This article proposes a novel method of utilizing side information by combining arbitrary nonlinear Quantile Regression model and Matrix Factorization under Bayesian framework. Gradient-free optimization procedure with the novel Surrogate Function is used to solve the resulting MAP estimator. The model performance has been evaluated on real data-sets.

Keywords: Matrix Factorization; Side information; Quantile Random Forest; Bayesian Quantile Regression; Machine learning; Majorization–minimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715220301504
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:165:y:2020:i:c:s0167715220301504

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2020.108847

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().