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Proceedings Paper

Exploiting context in kernel-mapping recommender system algorithms
Author(s): Mustansar Ali Ghazanfar; Adam Prϋgel-Bennett
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Paper Abstract

Making e ective recommendations from a domain consisting of millions of ratings is a major research challenge in the application of machine learning. Kernel Mapping Recommender (KMR) algorithms have been proposed providing state-of-the-art performance. In this paper, we show how context information can be added to KMR algorithms. We consider the trusted friends of a user as their social context and show how this information can be used to provide more personalised, refined, and trustworthy recommendations. The limited set of friends; however, restricts the amount of data available to create useful recommendations. This paper sheds light on this issue and specifically on the amount of friends necessary to get satisfactory recommendations.

Paper Details

Date Published: 24 December 2013
PDF: 5 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 906727 (24 December 2013); doi: 10.1117/12.2051416
Show Author Affiliations
Mustansar Ali Ghazanfar, Univ. of Engineering and Technology (Pakistan)
Adam Prϋgel-Bennett, Univ. of Southampton (United Kingdom)


Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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