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Kernel mapping recommender system algorithms

Record type: Article

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. In this paper, we propose a new algorithm that we call the Kernel-Mapping Recommender (KMR), which uses a novel structure learning technique. This paper makes the following contributions: we show how (1) user-based and item-based versions of the KMR algorithm can be built; (2) user-based and item-based versions can be combined; (3) more information—features, genre, etc.—can be employed using kernels and how this affects the final results; and (4) to make reliable recommendations under sparse, cold-start, and long tail scenarios. By extensive experimental results on five different datasets, we show that the proposed algorithms outperform or give comparable results to other state-of-the-art algorithms.

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Citation

Ghazanfar, Mustansar, Prugel-Bennett, Adam and Szedmak, Sandor (2012) Kernel mapping recommender system algorithms Information Sciences, 208, pp. 81-104. (doi:10.1016/j.ins.2012.04.012).

More information

e-pub ahead of print date: 5 May 2012
Published date: November 2012
Keywords: recommender systems, structure learning, linear operation, maximum margin, kernel
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272686
URI: http://eprints.soton.ac.uk/id/eprint/272686
ISSN: 0020-0255
PURE UUID: 90f98920-47ad-4096-b834-8544043d8c78

Catalogue record

Date deposited: 21 Aug 2011 14:30
Last modified: 18 Jul 2017 06:21

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Contributors

Author: Mustansar Ghazanfar
Author: Adam Prugel-Bennett
Author: Sandor Szedmak

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