Are we there yet? Estimating training time for recommendation systems

Iulia Paun, Yashar Moshfeghi, Nikos Ntarmos

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 1st Workshop on Machine Learning and Systems, EuroMLSys 2021
Subtitle of host publicationProceedings of the 1st Workshop on Machine Learning and Systems
Place of PublicationNew York
Pages39-47
Number of pages9
ISBN (Electronic)9781450382984
DOIs
Publication statusPublished - 26 Apr 2021

Publication series

NameProceedings of the 1st Workshop on Machine Learning and Systems, EuroMLSys 2021

Keywords

  • recommendation systems
  • sampling-based processing time prediction
  • information retrieval

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