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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Szabó, Z., Póczos, B., Lőrincz, A. (2012). Collaborative Filtering via Group-Structured Dictionary Learning. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_31
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DOI: https://doi.org/10.1007/978-3-642-28551-6_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
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