Are we there yet? Estimating Training Time for Recommendation Systems

Paun, I., Moshfeghi, Y. and Ntarmos, N. (2021) Are we there yet? Estimating Training Time for Recommendation Systems. In: EuroMLSys ’21, 26 Apr 2021, pp. 39-47. ISBN 9781450382984 (doi: 10.1145/3437984.3458832)

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Recommendation systems (RS) are a key component of modern commercial platforms, with Collaborative Filtering (CF) based RSs being the centrepiece. Relevant research has long focused on measuring and improving the effectiveness of such CF systems, but alas their efficiency - especially with regards to their time- and resource-consuming training phase - has received little to no attention. This work is a first step in the direction of addressing this gap. To do so, we first perform a methodical study of the computational complexity of the training phase for a number of highly popular CF-based RSs, including approaches based on matrix factorisation, k-nearest neighbours, co-clustering, and slope one schemes. Based on this, we then build a simple yet effective predictor that, given a small sample of a dataset, is able to predict training times over the complete dataset. Our systematic experimental evaluation shows that our approach outperforms state-of-the-art regression schemes by a considerable margin.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Ntarmos, Dr Nikos and Paun, Ms Iulia and Moshfeghi, Dr Yashar
Authors: Paun, I., Moshfeghi, Y., and Ntarmos, N.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2021 Association for Computing Machinery
First Published:First published in EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems
Publisher Policy:Reproduce in accordance with the publisher copyright policy

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