Kernel mapping recommender system algorithms

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


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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.

Item Type: Article
ISSNs: 0020-0255 (print)
Keywords: recommender systems, structure learning, linear operation, maximum margin, kernel
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 272686
Date Deposited: 21 Aug 2011 14:30
Last Modified: 27 Mar 2014 20:18
Contact Email Address:
Further Information:Google Scholar
ISI Citation Count:1

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