SimuRec: Workshop on synthetic data and simulation methods for recommender systems research

Michael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, Manel Slokom

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)
114 Downloads (Pure)

Abstract

There is significant interest lately in using synthetic data and simulation infrastructures for various types of recommender systems research. However, there are not currently any clear best practices around how best to apply these methods. We proposed a workshop to bring together researchers and practitioners interested in simulating recommender systems and their data to discuss the state of the art of such research and the pressing open methodological questions. The workshop resulted in a report authored by the participants that documents currently-known best practices on which the group has consensus and lays out an agenda for further research over the next 3-5 years to fill in places where we currently lack the information needed to make methodological recommendations.

Original languageEnglish
Title of host publicationRecSys 2021 - 15th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery (ACM)
Pages803-805
Number of pages3
ISBN (Electronic)9781450384582
DOIs
Publication statusPublished - 2021
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands
Duration: 27 Sept 20211 Oct 2021

Publication series

NameRecSys 2021 - 15th ACM Conference on Recommender Systems

Conference

Conference15th ACM Conference on Recommender Systems, RecSys 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • Evaluation
  • Simulation
  • Synthetic data

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