Abstract
K-Nearest Neighbors algorithm (KNN) is the core of a considerable amount of online services and applications, like recommendation engines, content-classifiers, information retrieval systems, etc. The users of these services change their preferences over time, aggravating the computational challenges of KNN. In this work, we present UpKNN: an efficient thread-based out-of-core approach to take the updates of users preferences into account while it computes the KNN efficiently.
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Acknowledgments
This work was partially funded by Conicyt/Beca Doctorado en el Extranjero Folio 72140173 and Google Focused Award Web Alter-Ego.
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Kermarrec, AM., Mittal, N., Olivares, J. (2017). Multithreading Approach to Process Real-Time Updates in KNN Algorithms. In: El Abbadi, A., Garbinato, B. (eds) Networked Systems. NETYS 2017. Lecture Notes in Computer Science(), vol 10299. Springer, Cham. https://doi.org/10.1007/978-3-319-59647-1_9
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DOI: https://doi.org/10.1007/978-3-319-59647-1_9
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