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

Authors: 

Nava Tintarev, Shahin Rostami, Barry Smyth

Publication Type: 
Refereed Conference Meeting Proceeding
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
Conference Name: 
the 33rd Annual ACM Symposium
Digital Object Identifer (DOI): 
10.1145/3167132.3167419
Publication Date: 
22/04/2018
Research Group: 
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No