PAWS Meeting 2007-01-12

1st talk by Sharon:

Ko-Kang Chu, Maiga Chang and Yen-The Hsia Designing a Course Recommendation System on Web based on the Students’ course Selection Records

A system recommends non-mandatory courses to students based on the model of their topic preferences. The model of preferences is populated using the history of non-mandatory courses a student took earlier.

Every course belongs to several topics (relations are weighted). The system classifies student interests and recommends her new courses similar to the interesting ones.

The results of the systems evaluation do not support the prior hypothesis that the accuracy of recommendation would go up with the time.

Discussion:

Main criticism to the paper was concerning the idea of topic-based modeling of interests for courses recommendation. If a student took 3 courses on AI the system should think that she must have a strong interest in AI, but the student would not need any more AI courses – she already knows everything she needs by that time.

2nd talk by Tomek:

Nikolaus Bee, Helmut Prendinger, Arturo Nakasone, Elisabeth Andre, and Mitsuru IshizukaAutoSelect: What You Want Is What You Get: Real-Time Processing of Visual Attention and Affect

The paper presents a model and an experiment on using eye-tracking and physiological data (galvanic skin effect, blood rate) for prediction of user’s preferences. They try to build a system that predicts when a subject makes up his mind towards one of two options on the basis of changes in the unconscious behavior caused by building up the preferences (relation between the attention and emotions).

In the experiment a user chooses between two actions (prefer one tie over another).

Discussion:

Criticism:

- The cognitive model reported in the paper is questionable. What if users were presented not ties but shoes or laptops? What if the choice should be among 5 or 100 alternative? What if the alternatives are not similar (not only ties)?

- The experiment is affecting subjects. The system makes a guess, what tie a user would like to buy – it influences the results. What if a user was not sure about her choice? What if a user tries to be nice? Or vice versa – tries to contradict the system?

There were more comments during the discussion. If anyone remembers, please add them here.

How we can apply models like this in our work?

Two directions:

1. Using Eye-tracking as a research tool for:

- Systems (Interface) evaluation

- Modeling information:

a. As a supplementary source if modeling info, for example to prove that the user is working on what we think he is working -> we model them correctly (Conati)

b. As one of the main sources of modeling (as in the discussed paper)

2. Using eye-tracking as a novel interface in real applications.