Abstract
Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating them requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad to account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that can suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
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Mutlu, B., Veas, E., Trattner, C., Sabol, V. (2015). Towards a Recommender Engine for Personalized Visualizations. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_14
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DOI: https://doi.org/10.1007/978-3-319-20267-9_14
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