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Proceedings Paper

A scalable and practical one-pass clustering algorithm for recommender system
Author(s): Asra Khalid; Mustansar Ali Ghazanfar; Awais Azam; Saad Ali Alahmari
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Paper Abstract

KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.

Paper Details

Date Published: 8 December 2015
PDF: 6 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987526 (8 December 2015); doi: 10.1117/12.2229516
Show Author Affiliations
Asra Khalid, Univ. of Engineering and Technology, Taxila (Pakistan)
Mustansar Ali Ghazanfar, Univ. of Engineering and Technology, Taxila (Pakistan)
Awais Azam, Univ. of Engineering and Technology, Taxila (Pakistan)
Saad Ali Alahmari, Shaqra Univ. (Saudi Arabia)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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