Knowing the unknown: visualising consumption blind-spots in recommender system.

Nava, T., Rostami, S. and Smyth, B., 2018. Knowing the unknown: visualising consumption blind-spots in recommender system. In: SAC 2018 The 33rd ACM/SIGAPP Symposium On Applied Computing, 9-13 April 2018, Pau, France.

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Official URL: https://www.sigapp.org/sac/sac2018/

DOI: 10.1145/3167132.3167419

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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Official website: The ACM Digital Library https://dl.acm.org/dl.cfm List of all the SAC conferences available from https://dl.acm.org/event.cfm?id=RE133
Uncontrolled Keywords:Visualisation; Recommender Systems; Filter Bubble; Chord Diagram;
Group:Faculty of Science & Technology
ID Code:30801
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:08 Jun 2018 15:09
Last Modified:08 Jun 2018 15:09

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