Knowing the Unknown

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

Tintarev, N. (TU Delft Web Information Systems)
Rostami, Shahin (Bournemouth University)
Smyth, Barry (University College Dublin)

2018

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.

Visualisation
Recommender Systems
Filter Bubble
Chord Diagram

http://resolver.tudelft.nl/uuid:61c0f406-4506-46b6-ae77-2d11dd36f1fc

https://doi.org/10.1145/3167132.3167419

Association for Computer Machinery, New York

978-1-4503-5191-1

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

SAC 2018, 2018-04-09 → 2018-04-13, Pau, France

Accepted author manuscript

Institutional Repository

conference paper

© 2018 N. Tintarev, Shahin Rostami, Barry Smyth