Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/49392
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dc.contributor.authorAnelli, Vito Walterit
dc.contributor.authorBellogín, Alejandroit
dc.contributor.authorNoia, Tommaso Diit
dc.contributor.authorDonini, Francesco Mariait
dc.contributor.authorPaparella, Vincenzoit
dc.contributor.authorPomo, Claudioit
dc.date.accessioned2023-03-23T18:49:07Z-
dc.date.available2023-03-23T18:49:07Z-
dc.date.issued2022it
dc.identifier.urihttp://hdl.handle.net/2067/49392-
dc.description.abstractExplainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be the most easily applicable ones, since they treat as black boxes the increasingly complex recommendation models. Recent literature has shown that for post-hoc explanations based on local surrogate models, there are problems related to the robustness of the approach itself. This consideration becomes even more relevant in human-related tasks, from transparency or trustworthiness points of view – like recommendation. We show how the behavior of LIME-RS – a classical post-hoc model based on surrogates – is strongly model-dependent and does not prove to be accountable for the explanations generated.it
dc.format.mediumELETTRONICOit
dc.language.isoengit
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAn Analysis of Local Explanation with LIME-RSit
dc.typeconferenceObject*
dc.identifier.scopus2-s2.0-85136234963it
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85136234963it
dc.identifier.urlhttps://ceur-ws.org/Vol-3177/paper4.pdfit
dc.relation.journalCEUR WORKSHOP PROCEEDINGSit
dc.relation.ispartofbook12th Italian Information Retrieval Workshop, IIR 2022it
dc.relation.numberofpages8it
dc.relation.alleditorsPasi G., Cremonesi P., Orlando S., Zanker M., Zanker M., Massimo D., Turati G.it
dc.relation.conferencename12th Italian Information Retrieval Workshop, IIR 2022it
dc.relation.conferenceplaceMilan, Italyit
dc.relation.conferencedate29 June 2022 through 30 June 2022it
dc.relation.volume3177it
dc.subject.scientificsectorINF/01it
dc.subject.scientificsectorING-INF/05it
dc.subject.ercsectorPE6it
dc.description.internationalit
dc.contributor.countryITAit
dc.contributor.countryESPit
dc.type.refereeREF_1it
dc.type.invitednoit
dc.type.miur273*
dc.publisher.nameCEUR-WSit
dc.publisher.placeAachenit
dc.publisher.countryDEUit
item.fulltextWith Fulltext-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1613-0073-
crisitem.journal.anceE211129-
Appears in Collections:D1. Contributo in Atti di convegno
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