Abstract
We present a system which combines interactive visual analysis and recommender systems to support insight generation for the user. Our approach combines a stacked graph visualization with a content-based recommender algorithm, where promising views can be revealed to the user for further investigation. By exploiting both the current user navigational data and view properties, the system allows the user to focus on visual space in which she or he is interested. After testing with more than 30 users, we analyze the results and show that accurate user profiles can be generated based on user behavior and view property data.
Highlights
Due to the exponential growth of information in virtually all industries, the need for computational tools that support the analysis process is increasing
We raised the question of how recommending can be incorporated into an information visualization system for suggesting interesting views to the user
Aiming to alleviate the overload caused in the visual exploration of more than 10,000 views, we proposed a method for adapting a recommender engine to the stacked graph visualization
Summary
Due to the exponential growth of information in virtually all industries, the need for computational tools that support the analysis process is increasing. As stated by Thomas and Cook [1], our ability to collect data is increasing at a faster rate than our ability to analyze it. Recommender algorithms have been adapted to assist in this process. In the context of e-commerce systems, for example, we find methods that make item recommendations based on user profiles and item properties. Amazon and iTunes Store, for instance, are two popular e-commerce systems that have largely benefited from it. In the context of information visualization, we believe that recommender systems have not been sufficiently exploited
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