Eye-tracking study of user behavior in recommender interfaces

Li CHEN*, Pearl Pu

*Corresponding author for this work

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

56 Citations (Scopus)


Recommender systems, as a type of Web personalized service to support users' online product searching, have been widely developed in recent years but with primary emphasis on algorithm accuracy. In this paper, we particularly investigate the efficacy of recommender interface designs in affecting users' decision making strategies through the observation of their eye movements and product selection behavior. One interface design is the standard list interface where all recommended items are listed one by one. Another two are layout variations of organization-based interface where recommendations are grouped into categories. The eye-tracking user evaluation shows that the organization interfaces, especially the one with a quadrant layout, can significantly attract users' attentions to more items, with the resulting benefit to enhance their objective decision quality.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization - 18th International Conference, UMAP 2010, Proceedings
Number of pages6
Publication statusPublished - 2010
Event18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010 - Big Island, HI, United States
Duration: 20 Jun 201024 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010
Country/TerritoryUnited States
CityBig Island, HI

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • eye-tracking study
  • list interface
  • organization design
  • recommender systems
  • users' adaptive behavior


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