Workshop on reproducibility and replication in recommender systems evaluation - RepSys

Alejandro Bellogín, Pablo Castells, Alan Said, Domonkos Tikk

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

7 Citations (Scopus)

Abstract

Experiment replication and reproduction are key requirements for empirical research methodology, and an important open issue in the field of Recommender Systems. When an experiment is repeated by a different researcher and exactly the same result is obtained, we can say the experiment has been replicated. When the results are not exactly the same but the conclusions are compatible with the prior ones, we have a reproduction of the experiment. Reproducibility and replication involve recommendation algorithm implementations, experimental protocols, and evaluation metrics. While the problem of reproducibility and replication has been recognized in the Recommender Systems community, the need for a clear solution remains largely unmet, which motivates the present workshop.

Original languageEnglish
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages485-486
Number of pages2
DOIs
Publication statusPublished - 2013
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Other

Other7th ACM Conference on Recommender Systems, RecSys 2013
CountryChina
CityHong Kong
Period10/12/1310/16/13

Keywords

  • Evaluation
  • Experimental design
  • Experimental methodology
  • Replicability
  • Reproducibility

ASJC Scopus subject areas

  • Software

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  • Cite this

    Bellogín, A., Castells, P., Said, A., & Tikk, D. (2013). Workshop on reproducibility and replication in recommender systems evaluation - RepSys. In RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems (pp. 485-486). (RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems). https://doi.org/10.1145/2507157.2508006