Dynamics of human trust in recommender systems

Jason L. Harman, John O'Donovan, Tarek Abdelzaher, Cleotilde Gonzalez

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The trust that humans place on recommendations is key to the success of recommender systems. The formation and decay of trust in recommendations is a dynamic process influenced by context, human preferences, accuracy of recommendations, and the interactions of these factors. This paper describes two psychological experiments (N=400) that evaluate the evolution of trust in recommendations over time, under personalized and nonpersonalized recommendations by matching or not matching a participant's profile. Main findings include: Humans trust inaccurate recommendations more than they should; when recommendations are personalized, they lose trust in inaccurate recommendations faster than when recommendations are not personalized; and participants report less trust and lower overall ratings of personalized but inaccurate recommendations compared to not-personalized inaccurate recommendations. We make connections to the possible implications of these psychological findings to the design of recommender systems.

Original languageEnglish (US)
Title of host publicationRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450326681
StatePublished - Oct 6 2014
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: Oct 6 2014Oct 10 2014

Publication series

NameRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems


Other8th ACM Conference on Recommender Systems, RecSys 2014
Country/TerritoryUnited States
CityFoster City


  • Cognitive processes
  • Decision making
  • Trust

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems


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