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.
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.
Auteur(s)
Eric Danan1
, Thibault Gajdos2
, Jean-Marc Tallon3, 4
1
THEMA -
Théorie économique, modélisation et applications
( 1003463 )
- 33, boulevard du Port 95011 Cergy-Pontoise Cedex
- France
Centre National de la Recherche Scientifique UMR8184 ( 441569 )
;
CY Cergy Paris Université ( 1003413 )
2
LPC -
Laboratoire de psychologie cognitive
( 849 )
- Pôle 3 C, Case D 3 place Victor Hugo 13331 Marseille Cedex 3
- France