Collaborative filtering via group-structured dictionary learning
Zoltan Szabo, Barnabas Poczos and András Lorincz
In: The 10th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 12-15 March 2012, Tel-Aviv, Israel.

There is a more recent version of this eprint available. Click here to view it.

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 method outperforms its state-of-the-art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.

Available Versions of this Item

• Collaborative filtering via group-structured dictionary learning (deposited 28 January 2012) [Currently Displayed]
  • Collaborative filtering via group-structured dictionary learning (deposited 16 March 2012)

[Edit]