Collaborative Filtering via Group-Structured Dictionary Learning
Optimization and Control
2012-03-08 v1 Machine Learning
Statistics Theory
Machine Learning
Statistics Theory
Abstract
Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented technique outperforms its state-of-the-art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements.
Cite
@article{arxiv.1201.0341,
title = {Collaborative Filtering via Group-Structured Dictionary Learning},
author = {Zoltan Szabo and Barnabas Poczos and Andras Lorincz},
journal= {arXiv preprint arXiv:1201.0341},
year = {2012}
}
Comments
A compressed version of the paper has been accepted for publication at the 10th International Conference on Latent Variable Analysis and Source Separation (LVA/ICA 2012)