Vizrec: a two-stage recommender system for personalized visualizations

Belgin Mutlu, Eduardo Veas, Christoph Trattner, Vedran Sabol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer well-defined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach - VizRec - combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Intelligent User Interfaces Companion
Pages49-52
Number of pages4
Publication statusPublished - 2015

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