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).


[img] PDF (Abstract)
Download (44Kb)
PDF (Full preprint version)
Download (347Kb) | Preview


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
Digital Object Identifier (DOI): doi:10.1016/j.ins.2012.04.012
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 > Southampton Wireless Group
ePrint ID: 272686
Accepted Date and Publication Date:
November 2012Published
5 May 2012Made publicly available
Date Deposited: 21 Aug 2011 14:30
Last Modified: 31 Mar 2016 14:21
Further Information:Google Scholar

Actions (login required)

View Item View Item

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics