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  5. Knowing the Unknown: Visualising Consumption Blind-Spots in Recommender Systems
 
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Knowing the Unknown: Visualising Consumption Blind-Spots in Recommender Systems

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Author(s)
Tintarev, Nava 
Rostami, Shahin 
Smyth, Barry 
Uri
http://hdl.handle.net/10197/10165
Date Issued
13 April 2018
Date Available
25T11:54:00Z April 2019
Abstract
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
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2018 the Authors
Keywords
  • Visualisation

  • Recommender Systems

  • Filter Bubble

  • Chord Diagram

DOI
10.1145/3167132.3167419
Web versions
https://www.sigapp.org/sac/sac2018/
Language
English
Status of Item
Peer reviewed
Part of
SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing
Description
The 33rd Annual ACM/ SIGAPP Symposium on Applied Computing (SAC'18), Pau, France, 9-13 April 2018
ISBN
978-1-4503-5191-1
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
Scopus© citations
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