Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/49410
Title: The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models
Authors: Anelli, Vito Walter
Bellogín, Alejandro
Ferrara, Antonio
Malitesta, Daniele
Merra, Felice Antonio
Pomo, Claudio
Donini, Francesco Maria 
Di Sciascio, Eugenio
Di Noia, Tommaso
Journal: CEUR WORKSHOP PROCEEDINGS 
Issue Date: 2022
Abstract: 
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.
URI: http://hdl.handle.net/2067/49410
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:D1. Contributo in Atti di convegno

Files in This Item:
File Description SizeFormat Existing users please
SEDB-2022-Elliott.pdfPrinted Version1.08 MBAdobe PDF    Request a copy
Show full item record

Page view(s)

34
Last Week
0
Last month
1
checked on Mar 27, 2024

Download(s)

1
checked on Mar 27, 2024

Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons