Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach

Paul Seitlinger*, Tobias Ley, Dominik Kowald, Dieter Theiler, Ilire Hasani-Mavriqi, Sebastian Dennerlein, Elisabeth Lex, Dietrich Albert

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (N = 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.

Original languageEnglish
Pages (from-to)557-575
Number of pages19
JournalInternational Journal of Human-Computer Interaction
Issue number6
Publication statusPublished - 11 Oct 2018


  • collaborative search
  • exploration–exploitation tradeoff
  • Ideational fluency
  • reflective search framework
  • tag recommender
  • tagging consistency

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Human-Computer Interaction
  • Computer Science Applications


Dive into the research topics of 'Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach'. Together they form a unique fingerprint.

Cite this