A scalable and practical one-pass clustering algorithm for recommender system
Asra Khalid, Mustansar Ali Ghazanfar, Awais Azam, Saad Ali Alahmari · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
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.