Collaborative filtering via group-structured dictionary learning

Authors:
Zolt$#225/n Szabó/;Barnab$#225/s Pó/czos;Andr$#225/s Lő/rincz
Affiliations:
Faculty of Informatics, Eö/tvö/s Lor$#225/nd University, Budapest, Hungary;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Faculty of Informatics, Eö/tvö/s Lor$#225/nd University, Budapest, Hungary
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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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.