review-article

Report on the workshop on reproducibility and replication in recommender systems evaluation (RepSys)

Authors:

Alejandro Bellogín,

Pablo Castells,

Domonkos TikkAuthors Info & Claims

ACM SIGIR Forum, Volume 48, Issue 1

Pages 29 - 35

https://doi.org/10.1145/2641383.2641389

Published: 26 June 2014 Publication History

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 main questions addressed in the present workshop.

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Cited By

Polatidis NPapaleonidas APimenidis EIliadis L(2020)An explanation-based approach for experiment reproducibility in recommender systemsNeural Computing and Applications10.1007/s00521-019-04274-x32:16(12259-12266)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s00521-019-04274-x
Polatidis NPimenidis EFish AKapetanakis S(2019)A Guideline-Based Approach for Assisting with the Reproducibility of Experiments in Recommender Systems EvaluationInternational Journal on Artificial Intelligence Tools10.1142/S021821301960011X28:08(1960011)Online publication date: 2-Dec-2019
https://doi.org/10.1142/S021821301960011X
Polatidis NPimenidis E(2018)Reproduction of Experiments in Recommender Systems Evaluation Based on ExplanationsEngineering Applications of Neural Networks10.1007/978-3-319-98204-5_16(194-200)Online publication date: 27-Jul-2018
https://doi.org/10.1007/978-3-319-98204-5_16
Show More Cited By

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Report on the workshop on reproducibility and replication in recommender systems evaluation (RepSys)
1. General and reference

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Workshop on reproducibility and replication in recommender systems evaluation: RepSys
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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 ...
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Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system ...
RepSys '13: Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

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Information

Published In

cover image ACM SIGIR Forum

ACM SIGIR Forum Volume 48, Issue 1

June 2014

42 pages

ISSN:0163-5840

DOI:10.1145/2641383

Editors:
Fernando Diaz
Microsoft Research NYC, New York, NY
,
Raman Chandrasekar
Serials Solutions, Seattle, WA

Issue’s Table of Contents

Copyright © 2014 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2014

Published in SIGIR Volume 48, Issue 1

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Cited By

Polatidis NPapaleonidas APimenidis EIliadis L(2020)An explanation-based approach for experiment reproducibility in recommender systemsNeural Computing and Applications10.1007/s00521-019-04274-x32:16(12259-12266)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s00521-019-04274-x
Polatidis NPimenidis EFish AKapetanakis S(2019)A Guideline-Based Approach for Assisting with the Reproducibility of Experiments in Recommender Systems EvaluationInternational Journal on Artificial Intelligence Tools10.1142/S021821301960011X28:08(1960011)Online publication date: 2-Dec-2019
https://doi.org/10.1142/S021821301960011X
Polatidis NPimenidis E(2018)Reproduction of Experiments in Recommender Systems Evaluation Based on ExplanationsEngineering Applications of Neural Networks10.1007/978-3-319-98204-5_16(194-200)Online publication date: 27-Jul-2018
https://doi.org/10.1007/978-3-319-98204-5_16
Polatidis NKapetanakis SPimenidis EKosmidis K(2018)Reproducibility of Experiments in Recommender Systems EvaluationArtificial Intelligence Applications and Innovations10.1007/978-3-319-92007-8_34(401-409)Online publication date: 22-May-2018
https://doi.org/10.1007/978-3-319-92007-8_34
Cremonesi PSaid ATikk DZhou M(2016)Introduction to the Special Issue on Recommender System BenchmarkingACM Transactions on Intelligent Systems and Technology10.1145/28706277:3(1-4)Online publication date: 8-Mar-2016
https://dl.acm.org/doi/10.1145/2870627
Beel JBreitinger CLanger SLommatzsch AGipp B(2016)Towards reproducibility in recommender-systems researchUser Modeling and User-Adapted Interaction10.1007/s11257-016-9174-x26:1(69-101)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s11257-016-9174-x

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