Abstract
In this paper, we propose an efficient KNN service, called KPS (KNN-Peer-Sampling). The KPS service can be used in various contexts e.g. recommendation systems, information retrieval and data mining. KPS borrows concepts from P2P gossip-based clustering protocols to provide a localized and efficient KNN computation in large-scale systems. KPS is a sampling-based iterative approach, combining randomness, to provide serendipity and avoid local minimum, and clustering, to ensure fast convergence. We compare KPS against the state of the art KNN centralized computation algorithm NNDescent, on multiple datasets. The experiments confirm the efficiency of KPS over NNDescent: KPS improves significantly on the computational cost while converging quickly to a close to optimal KNN graph. For instance, the cost, expressed in number of pairwise similarity computations, is reduced by \(\approx \)23 % and \(\approx \)49 % to construct high quality KNN graphs for Jester and MovieLens datasets, respectively. In addition, the randomized nature of KPS ensures eventual convergence, not always achieved with NNDescent.
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Notes
- 1.
Note that the strength of KPS is to achieve good results with less information so this way of comparing is not in our favor.
References
Jester dataset. http://grouplens.org/datasets/jester/
Jester joke recommender. http://shadow.ieor.berkeley.edu/humor/
Movielens dataset. http://grouplens.org/datasets/movielens/
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Benkaouz, Y., Erradi, M., Kermarrec, AM. (2016). Nearest Neighbors Graph Construction: Peer Sampling to the Rescue. In: Abdulla, P., Delporte-Gallet, C. (eds) Networked Systems. NETYS 2016. Lecture Notes in Computer Science(), vol 9944. Springer, Cham. https://doi.org/10.1007/978-3-319-46140-3_4
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