PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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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.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:Official version: "http://dx.doi.org/10.1007/978-3-642-28551-6_31". Compressed version of "http://arxiv.org/abs/1201.0341".
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9506
Deposited By:Zoltan Szabo
Deposited On:16 March 2012

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