Rating support interfaces to improve user experience and recommender accuracy

  • Tien T. Nguyen
  • , Daniel Kluver
  • , Ting Yu Wang
  • , Pik Mai Hui
  • , Michael D. Ekstrand
  • , Martijn C. Willemsen
  • , John Riedl

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

44 Scopus citations

Abstract

One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to ex- ternal measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rating decisions using exemplars. In our study, we introduce interfaces that provide these methods of support. We also present a set of methodologies to evaluate the efficacy of the new interfaces via a user experiment. Our results suggest that presenting exemplars during the rating process helps users rate more consistently, and increases the quality of the data.

Original languageEnglish (US)
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages149-156
Number of pages8
DOIs
StatePublished - 2013
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Other

Other7th ACM Conference on Recommender Systems, RecSys 2013
Country/TerritoryChina
CityHong Kong
Period10/12/1310/16/13

Keywords

  • Cognitive load
  • Experimentation
  • Human factors
  • Inter- face design
  • Preference elicitation
  • Recommendation accuracy
  • User experience

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