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

Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering
Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering
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. Hybrid 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.
Ghazanfar, Mustansar
9452876d-46e5-4c7d-9120-c486f448632d
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Ghazanfar, Mustansar
9452876d-46e5-4c7d-9120-c486f448632d
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

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. Hybrid 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.

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Published date: 19 August 2010
Organisations: Southampton Wireless Group

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

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Date deposited: 19 Aug 2010 17:32
Last modified: 16 Dec 2019 20:40

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