Making recommendations better: An analytic model for Human-Recommender Interaction

Sean M. McNee, John Riedl, Joseph A Konstan

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

86 Scopus citations

Abstract

Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.

Original languageEnglish (US)
Title of host publicationCHI'06 Extended Abstracts on Human Factors in Computing Systems, CHI EA'06
Pages1103-1108
Number of pages6
DOIs
StatePublished - Dec 1 2006
EventConference on Human Factors in Computing Systems, CHI EA 2006 - Montreal, QC, Canada
Duration: Apr 22 2006Apr 27 2006

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Other

OtherConference on Human Factors in Computing Systems, CHI EA 2006
CountryCanada
CityMontreal, QC
Period4/22/064/27/06

Keywords

  • Collaborative filtering
  • Human-Recommender Interaction
  • Information seeking
  • Personalization
  • Recommender systems
  • User-centered design

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