Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2229516 | Proceedings of SPIE |
Keywords | Field | DocType |
Recommender systems,Collaborative filtering,K-Means clustering,Online clustering | Recommender system,k-means clustering,Data mining,Canopy clustering algorithm,CURE data clustering algorithm,Collaborative filtering,Data stream clustering,Correlation clustering,Computer science,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
9875 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asra Khalid | 1 | 34 | 3.54 |
| Mustansar Ali Ghazanfar | 2 | 25 | 6.27 |
| Muhammad Awais Azam | 3 | 178 | 24.45 |
| Saad Ali Alahmari | 4 | 0 | 0.34 |