The University of Southampton
University of Southampton Institutional Repository

Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering

Ghazanfar, Mustansar and Prugel-Bennett, Adam (2010) Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering IAENG International Journal of Computer Science, 37, (3)

Record type: Article

Abstract

Recommender systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. Moreover, machine learning classifiers can be used for recommendation by training them on content information. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hy- brid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed unique generalized switching hybrid recommendation algorithms that combine machine learning classifiers with the collaborative filtering recommender systems. Experimental results on two different data sets, show that the proposed algorithms are scalable and provide better performance—in terms of accuracy and coverage—than other algorithms while at the same time eliminate some recorded problems with the recommender systems.

PDF IJCS_37_3_09.pdf - Version of Record
Download (838kB)

More information

Published date: 19 August 2010
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271493
URI: http://eprints.soton.ac.uk/id/eprint/271493
PURE UUID: 34ec192c-9121-4c66-806e-ef6fee125566

Catalogue record

Date deposited: 19 Aug 2010 17:32
Last modified: 18 Jul 2017 06:42

Export record

Contributors

Author: Mustansar Ghazanfar
Author: Adam Prugel-Bennett

University divisions

Download statistics

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×