Comments

Hi YanZhao,
Thank you very much for reading my page and your kind suggestion:

1. In the first paragraph of Introduction I pointed out its association with AI. Machine learning (ML) models (classifiers) are the core elements of adaptive systems, and ML is categorized under AI. Machine learning (ML) models (classifiers) are the main core of adaptive systems, and ML is categorized under AI. For example, in one study, they analyzed eye-gaze data to create user- and task model; they used a classifier to classify different affective states based on the eye-gaze features related to the area of interests in a visualization. In every U-AIV system, there is a user or task modeling phase, which provides the basis upon which they can decide when to present the adaptive visualizations or adaptive support in general. In the first study those highlighting interventions, and the second study, those feedbacks should be based upon a user model or task model and that's where we can see the most AI techniques and ML models are used. The other AI’s direct techniques and artifacts such as sequence planning models (HMM, Max Entropy Markov Model, etc.), all come very handy when we discuss the advanced type of problems in User Adaptive Information Visualization. However, as the purpose of this page was to introduce the field by two papers from two different research teams, I decided to select these two papers, because they present a nice introduction to the type of problems that are investigated in U-AIV, and without this foundation, discussing the advanced problems would be a bit pointless.
2. I just added a dedicated section (under introduction), in which, I elaborated this relationship.

Thanks again for your valuable suggestion ;)
Yaashaar