dc.contributor.author | Deldjoo, Yashar | |
dc.contributor.author | Bellogin Kouki, Alejandro | |
dc.contributor.author | Di Noia, Tommaso | |
dc.contributor.other | UAM. Departamento de Ingeniería Informática | es_ES |
dc.date.accessioned | 2022-03-11T11:29:45Z | |
dc.date.available | 2022-03-11T11:29:45Z | |
dc.date.issued | 2021-07-01 | |
dc.identifier.citation | Information Processing & Management 58.5 (2021): 102662 | es_ES |
dc.identifier.issn | 0306-4573 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10486/700693 | |
dc.description.abstract | The impact of data characteristics on the performance of classical recommender systems has
been recently investigated and produced fruitful results about the relationship they have with
recommendation accuracy. This work provides a systematic study on the impact of broadly
chosen data characteristics (DCs) of recommender systems. This is applied to the accuracy
and fairness of several variations of CF recommendation models. We focus on a suite of DCs
that capture properties about the structure of the user–item interaction matrix, the rating
frequency, item properties, or the distribution of rating values. Experimental validation of
the proposed system involved large-scale experiments by performing 23,400 recommendation
simulations on three real-world datasets in the movie (ML-100K and ML-1M) and book domains
(BookCrossing). The validation results show that the investigated DCs in some cases can have
up to 90% of explanatory power – on several variations of classical CF algorithms –, while they
can explain – in the best case – about 40% of fairness results (measured according to user
gender and age sensitive attributes). Therefore, this work evidences that it is more difficult to
explain variations in performance when dealing with fairness dimension than accuracy | es_ES |
dc.description.sponsorship | This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades (reference: PID2019-108965GB-I00),
and in part by H2020 PASSEPARTOUT No. 101016956, Servizi Locali S.P.A. Servizi Locali 2.0, and Ministry of Education, University
and Research (references: PON ARS01_00876 Bio-D, PON ARS01_00821 FLET4.0, PON ARS01_00917 OK-INSAID) | en_US |
dc.format.extent | 26 pag. | es_ES |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Information Processing & Management | en_US |
dc.rights | © 2021 by the authors | es_ES |
dc.subject.other | Explanatory power | en_US |
dc.subject.other | Fairness | en_US |
dc.subject.other | Accuracy | en_US |
dc.subject.other | Collaborative filtering | en_US |
dc.subject.other | Data characteristics | en_US |
dc.title | Explaining recommender systems fairness and accuracy through the lens of data characteristics | en_US |
dc.type | article | en_US |
dc.subject.eciencia | Informática | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.ipm.2021.102662 | es_ES |
dc.identifier.doi | 10.1016/j.ipm.2021.102662 | es_ES |
dc.identifier.publicationfirstpage | 102662-1 | es_ES |
dc.identifier.publicationissue | 5 | es_ES |
dc.identifier.publicationlastpage | 102662-26 | es_ES |
dc.identifier.publicationvolume | 58 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/101016956/EU//PASSEPARTOUT | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | en |
dc.rights.cc | Reconocimiento – NoComercial – SinObraDerivada | |
dc.rights.accessRights | openAccess | es_ES |
dc.authorUAM | Bellogín, Alejandro (261320) | |
dc.facultadUAM | Escuela Politécnica Superior | |