Inferring users’ critiquing feedback on recommendations from eye movements

Li CHEN*, Feng Wang, Wen Wu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)


In recommender systems, critiquing has been popularly applied as an effective approach to obtaining users’ feedback on recommended products. In order to reduce users’ efforts of creating critiquing criteria on their own, some systems have aimed at suggesting critiques for users to choose. How to accurately match system-suggested critiques to users’ intended feedback hence becomes a challenging issue. In this paper, we particularly take into account users’ eye movements on recommendations to infer their critiquing feedback. Based on a collection of real users’ eye-gaze data, we have demonstrated the approach’s feasibility of implicitly deriving users’ critiquing criteria. It hence indicates a promising direction of using eye-tracking technique to improve existing critique suggestion methods.

Original languageEnglish
Title of host publicationCase-Based Reasoning Research and Development - 24th International Conference, ICCBR 2016, Proceedings
EditorsM. Belen Diaz-Agudo, Thomas Roth-Berghofer, Ashok Goel
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319470955
Publication statusPublished - 2016
Event24th International Conference on Case-Based Reasoning Research and Development, ICCBR 2016 - Atlanta, United States
Duration: 31 Oct 20162 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9969 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Case-Based Reasoning Research and Development, ICCBR 2016
Country/TerritoryUnited States

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Critiquing feedback
  • Eye movements
  • Feedback inference
  • Fixation metrics
  • Recommender systems


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