Tailored Recommendations
In: ISSN: 0176-1714, 2023
Online
academicJournal
Zugriff:
International audience ; Many popular internet platforms use so-called collaborative filtering systems to give personalized recommendations to their users, based on other users who provided similar ratings for some items. We propose a novel approach to such recommendation systems by viewing a recommendation as a way to extend an agent's expressed preferences, which are typically incomplete, through some aggregate of other agents' expressed preferences. These extension and aggregation requirements are expressed by an Acceptance and a Pareto principle, respectively. We characterize the recommendation systems satisfying these two principles and contrast them with collaborative filtering systems, which typically violate the Pareto principle.
Titel: |
Tailored Recommendations
|
---|---|
Autor/in / Beteiligte Person: | Danan, Eric ; Gajdos, Thibault ; Tallon, Jean-Marc ; Théorie économique, modélisation et applications (THEMA) ; Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY) ; Laboratoire de psychologie cognitive (LPC) ; Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS) ; Paris School of Economics (PSE) ; Université Paris 1 Panthéon-Sorbonne (UP1)-École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École des hautes études en sciences sociales (EHESS)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) ; Paris Jourdan Sciences Economiques (PJSE) ; ANR-17-CE26-0003,CHOp,Appréhender des Opinions Hétérogènes(2017) ; ANR-17-EURE-0001,PGSE,Ecole d'Economie de Paris(2017) |
Link: | |
Zeitschrift: | ISSN: 0176-1714, 2023 |
Veröffentlichung: | HAL CCSD ; Springer Verlag, 2023 |
Medientyp: | academicJournal |
DOI: | 10.1007/s00355-020-01295-7 |
Schlagwort: |
|
Sonstiges: |
|