Putting users in control of their recommendations

F. Maxwell Harper, Funing Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, Loren Terveen

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

43 Scopus citations

Abstract

The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like "show more popular items". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.

Original languageEnglish (US)
Title of host publicationRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages3-10
Number of pages8
ISBN (Electronic)9781450336925
DOIs
StatePublished - Sep 16 2015
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: Sep 16 2015Sep 20 2015

Publication series

NameRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems

Other

Other9th ACM Conference on Recommender Systems, RecSys 2015
CountryAustria
CityVienna
Period9/16/159/20/15

Keywords

  • Collaborative filtering
  • MovieLens
  • Personalization
  • Recommender systems
  • Simulation study
  • Social computing
  • User control
  • User study

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