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

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

doi:10.1145/3460231.3470938

SimuRec

Title

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

Author

Ekstrand, Michael D. (Boise State University)
Chaney, Allison (Duke University)
Castells, Pablo (Campus de Cantoblanco)
Burke, Robin (University of Colorado)
Rohde, David (Criteo)
Slokom, M. (TU Delft Multimedia Computing)

Date

2021

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.

Subject

Evaluation
Simulation
Synthetic data

To reference this document use:

http://resolver.tudelft.nl/uuid:5692f3c6-8f24-4fd7-b48c-8212b290f657

DOI

https://doi.org/10.1145/3460231.3470938

Publisher

Association for Computing Machinery (ACM)

ISBN

9781450384582

Source

RecSys 2021 - 15th ACM Conference on Recommender Systems