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

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Title: Knowing the Unknown: Visualising Consumption Blind-Spots in Recommender Systems
Authors: Tintarev, Nava
Rostami, Shahin
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/10165
Date: 13-Apr-2018
Online since: 2019-04-25T11:54:00Z
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: VisualisationRecommender SystemsFilter BubbleChord Diagram
DOI: 10.1145/3167132.3167419
Other versions: https://www.sigapp.org/sac/sac2018/
Language: en
Status of Item: Peer reviewed
Is part of: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing
Conference Details: The 33rd Annual ACM/ SIGAPP Symposium on Applied Computing (SAC'18), Pau, France, 9-13 April 2018
ISBN: 978-1-4503-5191-1
Appears in Collections:Insight Research Collection

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