Tailored Recommendations


  • Danan Eric
  • Gajdos Thibault
  • Tallon Jean-Marc


  • Recommendation systems
  • Incomplete preferences
  • Extension
  • Aggregation
  • Pareto principle
  • Collaborative filtering

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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.

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