The Challenging Reproducibility Taskin Recommender Systems Research between Traditional and Deep Learning Models
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
CEUR WorkshopDate
2022-06Citation
The 30th Italian Symposium on Advanced Database Systems, Tirrenia (PI), Italy, June 19-22, 2022ISSN
1613-0073Subjects
Recommender Systems; Reproducibility; Adversarial Learning; Visual Recommenders; Knowledge Graphs; InformáticaRights
© 2022 Copyright for this paper by its authorsAbstract
Recommender Systems have shown to be a useful tool for reducing over choice and providing accurate, personalized suggestions. The large variety of available recommendation algorithms, splitting techniques, assessment protocols, metrics, and tasks, on the other hand, has made thorough experimental evaluation extremely difficult. Elliot is a comprehensive framework for recommendation with the goal of running and reproducing a whole experimental pipeline from a single configuration file. The framework uses a variety of ways to load, filter, and divide data. Elliot optimizes hyper-parameters for a variety of recommendation algorithms, then chooses the best models, compares them to baselines, computes metrics ranging from accuracy to beyond-accuracy, bias, and fairness, and does statistical analysis. The aim is to provide researchers with a tool to ease all the experimental evaluation phases (and make them reproducible), from data reading to results collection. Elliot is freely available on GitHub at https://github.com/sisinflab/elliot.
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Google Scholar:Anelli, Vito Walter
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Bellogin Kouki, Alejandro
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Ferrara, Antonio
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Malitesta, Daniele
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Merra, Felice Antonio
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Pomo, Claudio
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Donini, Francesco Maria
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Di Sciascio, Eugenio
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Noia, Tommaso Di
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