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An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering

Record type: Conference or Workshop Item (Paper)

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. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommendation approach by combining a Naive Bayes classification approach with the collaborative filtering. Experimental results on two different data sets, show that the proposed algorithm is scalable and provide better performance – in terms of accuracy and coverage – than other algorithms while at the same time eliminates some recorded problems with the recommender systems.

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Citation

Ghazanfar, Mustansar and Prugel-Bennett, Adam (2010) An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering At The 2010 IAENG International Conference on Data Mining and Applications. 17 - 19 Mar 2010.

More information

Published date: 20 April 2010
Additional Information: Event Dates: 17-19 March, 2010
Venue - Dates: The 2010 IAENG International Conference on Data Mining and Applications, 2010-03-17 - 2010-03-19
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 268483
URI: http://eprints.soton.ac.uk/id/eprint/268483
PURE UUID: 54603019-b3f1-48f6-9987-2f9a5be6712e

Catalogue record

Date deposited: 08 Feb 2010 19:51
Last modified: 18 Jul 2017 06:54

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Contributors

Author: Mustansar Ghazanfar
Author: Adam Prugel-Bennett

University divisions


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