On sampling collaborative filtering datasets

Noveen Sachdeva, Carole Jean Wu, Julian Mcauley

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

Abstract

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance. Following this observation, this paper makes three main contributions: (1) characterizing the effect of sampling on algorithm performance, in terms of algorithm and dataset characteristics (e.g. sparsity characteristics, sequential dynamics, etc.); (2) designing SVP-CF, which is a data-specific sampling strategy, that aims to preserve the relative performance of models after sampling, and is especially suited to long-tailed interaction data; and (3) developing an oracle, DATA-GENIE, which can suggest the sampling scheme that is most likely to preserve model performance for a given dataset. The main benefit of DATA-GENIE is that it will allow recommender system practitioners to quickly prototype and compare various approaches, while remaining confident that algorithm performance will be preserved, once the algorithm is retrained and deployed on the complete data. Detailed experiments show that using DATA-GENIE, we can discard upto 5x more data than any sampling strategy with the same level of performance.

Original languageEnglish (US)
Title of host publicationWSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages842-850
Number of pages9
ISBN (Electronic)9781450391320
DOIs
StatePublished - Feb 11 2022
Externally publishedYes
Event15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual, Online, United States
Duration: Feb 21 2022Feb 25 2022

Publication series

NameWSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining

Conference

Conference15th ACM International Conference on Web Search and Data Mining, WSDM 2022
Country/TerritoryUnited States
CityVirtual, Online
Period2/21/222/25/22

Keywords

  • Benchmarking
  • Coreset mining
  • Large-scale learning
  • Sampling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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