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dc.contributor.authorIrurozki, E.
dc.contributor.authorPérez, A. 
dc.contributor.authorLobo, J.L.
dc.contributor.authorDel Ser, J. 
dc.date.accessioned2022-04-13T17:35:26Z
dc.date.available2022-04-13T17:35:26Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1466
dc.description.abstractThe problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner. The bottleneck of this process is a rank aggregation problem. We propose a generalization of the Borda algorithm for non-stationary ranking streams. As a main result, we bound the minimum number of samples required to output the ground truth with high probability. Besides, we show how the optimal parameters are set. Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more reliable than others. We show that, under mild assumptions, this generalization can solve the problem of rank aggregation over non-stationary data streams.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectPreference learningen_US
dc.subjectRank aggregationen_US
dc.subjectEvolving preferencesen_US
dc.subjectConcept Driften_US
dc.titleRank aggregation for non-stationary data streamsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.relation.publisherversionhttps://2021.ecmlpkdd.org/wp-content/uploads/2021/07/ranking_streams__concept_drift_and_uBorda-6.pdfen_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/1PE/TIN2017-82626-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleEuropean Conference on Machine Learningen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España