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Novel centroid selection approaches for KMeans-clustering based recommender systems

Novel centroid selection approaches for KMeans-clustering based recommender systems
Novel centroid selection approaches for KMeans-clustering based recommender systems
Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided.
recommender systems, collaborative filtering, k-means clustering, centroid (seed) selection in k-means clustering
0020-0255
1-33
Zahra, Sobia
f6713426-f4c5-478c-967a-557b29b68438
Ghazanfar, Mustansar Ali
d188e6f7-ad66-46e9-ad86-dfff2a5d8b78
Khalid, Asra
5e84ffeb-830d-4e97-84bc-fd0b9af0341c
Azam, Muhammad Awais
c21900a4-612c-4bec-acc7-693d8dc6a035
Naeem, Usman
f110bb2d-943c-445d-8a61-6ccd8de80c10
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Zahra, Sobia
f6713426-f4c5-478c-967a-557b29b68438
Ghazanfar, Mustansar Ali
d188e6f7-ad66-46e9-ad86-dfff2a5d8b78
Khalid, Asra
5e84ffeb-830d-4e97-84bc-fd0b9af0341c
Azam, Muhammad Awais
c21900a4-612c-4bec-acc7-693d8dc6a035
Naeem, Usman
f110bb2d-943c-445d-8a61-6ccd8de80c10
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Zahra, Sobia, Ghazanfar, Mustansar Ali, Khalid, Asra, Azam, Muhammad Awais, Naeem, Usman and Prugel-Bennett, Adam (2015) Novel centroid selection approaches for KMeans-clustering based recommender systems. Information Sciences, 1-33. (doi:10.1016/j.ins.2015.03.062).

Record type: Article

Abstract

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided.

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Zahra_Novel.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 27 March 2015
e-pub ahead of print date: 8 May 2015
Keywords: recommender systems, collaborative filtering, k-means clustering, centroid (seed) selection in k-means clustering
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 376972
URI: https://eprints.soton.ac.uk/id/eprint/376972
ISSN: 0020-0255
PURE UUID: 070c6980-e17d-49f4-b7c2-d07a79bec592

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Date deposited: 13 May 2015 10:58
Last modified: 18 Jul 2017 04:30

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Contributors

Author: Sobia Zahra
Author: Mustansar Ali Ghazanfar
Author: Asra Khalid
Author: Muhammad Awais Azam
Author: Usman Naeem
Author: Adam Prugel-Bennett

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