A scalable and practical one-pass clustering algorithm for recommender system

Asra Khalid; Mustansar Ali Ghazanfar; Awais Azam; Saad Ali Alahmari

doi:10.1117/12.2229516

8 December 2015 A scalable and practical one-pass clustering algorithm for recommender system

Asra Khalid, Mustansar Ali Ghazanfar, Awais Azam, Saad Ali Alahmari

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 987526 (2015) https://doi.org/10.1117/12.2229516
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

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.

Citation Download Citation

Asra Khalid, Mustansar Ali Ghazanfar, Awais Azam, and Saad Ali Alahmari "A scalable and practical one-pass clustering algorithm for recommender system", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987526 (8 December 2015); https://doi.org/10.1117/12.2229516

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