Knowing the Unknown: Visualising Consumption Blind-Spots in Recommender System

Nava Tintarev, Shahin Rostami, Barry Smyth

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

16 Citations (Scopus)
35 Downloads (Pure)


In this paper we consider how to help users to better understand their consumption profiles by examining two approaches to visualising user profiles - chord diagrams, and bar charts - aimed at revealing to users those regions of the recommendation space that are unknown to them, i.e. blind-spots. Both visualisations do this by connecting profile preferences with a filtered recommendation space. We compare and contrast the two visualisations in a live user study (n = 70). The results suggest that, although users can understand both visualisations, chord diagrams are particularly effective in helping users to identify blind-spots, while simpler bar charts are better for conveying what was already known in a profile. Evaluating the understandability of blind-spot visualizations is a first step toward using visual explanations to help address a criticism of recommender systems: that personalising information creates filter bubbles.
Original languageEnglish
Title of host publicationSAC '18
Subtitle of host publicationProceedings of the 33rd Annual ACM Symposium on Applied Computing
Place of PublicationNew York
PublisherAssociation for Computer Machinery
Number of pages4
ISBN (Print)978-1-4503-5191-1
Publication statusPublished - 2018
EventSAC 2018: The 33rd ACM/SIGAPP Symposium On Applied Computing - Pau, France
Duration: 9 Apr 201813 Apr 2018
Conference number: 33rd


ConferenceSAC 2018
Abbreviated titleSAC'18

Bibliographical note

Accepted author manuscript


  • Visualisation
  • Recommender Systems
  • Filter Bubble
  • Chord Diagram


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