Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
Replicable Evaluation of Recommender Systems
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Association for Computing Machinery, Inc.Date
2015-09-16Citation
10.1145/2792838.2792841
RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems. New York: ACM, 2015. 363 - 364
ISBN
978-145-033-692-5DOI
10.1145/2792838.2792841Funded by
Supported in part by the Ministerio de Educación y Ciencia (TIN2013-47090-C3-2).Project
Gobierno de España. TIN2013-47090-C3-2Editor's Version
http://dx.doi.org/10.1145/2792838.2792841Subjects
Evaluation; Experimental design; Experimental methodology; Replicability; Reproducibility; InformáticaNote
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '15 Proceedings of the 9th ACM Conference on Recommender Systems, http://dx.doi.org/10.1145/2792838.2792841.Rights
Copyright is held by the owner/author(s)Abstract
Recommender systems research is by and large based on comparisons
of recommendation algorithms’ predictive accuracies: the
better the evaluation metrics (higher accuracy scores or lower predictive
errors), the better the recommendation algorithm. Comparing
the evaluation results of two recommendation approaches
is however a difficult process as there are very many factors to be
considered in the implementation of an algorithm, its evaluation,
and how datasets are processed and prepared.
This tutorial shows how to present evaluation results in a clear
and concise manner, while ensuring that the results are comparable,
replicable and unbiased. These insights are not limited to recommender
systems research alone, but are also valid for experiments
with other types of personalized interactions and contextual information
access.
Files in this item
Google Scholar:Said, Alan
-
Bellogin Kouki, Alejandro
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