@article{Mutlu2016,

title = {VizRec: Recommending Personalized Visualizations},

author = {Belgin Mutlu and Eduardo Veas and Christoph Trattner},

url = {https://www.christophtrattner.info/pubs/ACM-TIIS.pdf},

year  = {2016},

date = {2016-01-01},

journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)},

volume = {6},

number = {4},

pages = {1-40},

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 the appropriate

representation requires specific expertise of the domain and underlying data. Our quest in this paper is to

study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has

to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a 

visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects

of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can

we capture and use those aspects to recommend visualizations? This paper investigates strategies to

 recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to

estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts

are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines

information on what a chart is about (tags) and how good it is (ratings). We present the design principles

behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a

crowd sourced study, and the analysis of strategies for visualization recommendation.},

note = {Pre SFI},

keywords = {CCS Concepts, Collaborative filtering;, Content ranking, Human-centered computing, Information Systems, Personalization},

pubstate = {published},

tppubtype = {article}

}