VizRec: A Two-Stage Recommender System for Personalized Visualizations

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.