Tailored Recommendations

Eric Danan, Thibault Gajdos () and Jean-Marc Tallon
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Thibault Gajdos: LPC - Laboratoire de psychologie cognitive - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Keywords: Recommendation systems; Incomplete preferences; Extension; Aggregation; Pareto principle; Collaborative filtering (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ict
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02973924v1
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Published in Social Choice and Welfare, 2023, 60, pp.15-34. ⟨10.1007/s00355-020-01295-7⟩

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Related works:
Journal Article: Tailored recommendations (2023)
Working Paper: Tailored Recommendations (2023)
Working Paper: Tailored Recommendations (2019)
Working Paper: Tailored Recommendations (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-02973924

DOI: 10.1007/s00355-020-01295-7

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