PAWS Meeting 2007-01-19
1st Talk by Joerg Brunstein
Topic: Eye movements as a window to the mind.
Summary:
the paper is mainly explaining the experiment on eye tracking.
they test on people in light, in dark, retell and describe the story after it's being told. Then measure their eye movements by looking at a whiteboard, contrast to the case of looking at the picture itself.
Discussion:
how does this experiment fit into our visualization interfaces design?
how do we do it differently in our studies?
possible solution: measure the eye movements from context and annotation, use the eye tracking result as a confirmation checking.
2nd Talk by Chirayu Wongchokprasitti
Tpoic: NewsMe: A Case Study for Adaptive News Systems with Open User Model (Preliminary Examination Rehearsal)
Summary:
NewsMe is a Personalized News Access System.
It allows users to provide feedbacks about theirs interests in news. Feedbacks are further used to construct the user model and recommend relevant news articles to users. It currently retrieves news from 82 RSS news feeds from 21 sources.
Discussion/Suggestion:
Topic: Eye movements as a window to the mind.
Summary:
the paper is mainly explaining the experiment on eye tracking.
they test on people in light, in dark, retell and describe the story after it's being told. Then measure their eye movements by looking at a whiteboard, contrast to the case of looking at the picture itself.
Discussion:
how does this experiment fit into our visualization interfaces design?
how do we do it differently in our studies?
possible solution: measure the eye movements from context and annotation, use the eye tracking result as a confirmation checking.
2nd Talk by Chirayu Wongchokprasitti
Tpoic: NewsMe: A Case Study for Adaptive News Systems with Open User Model (Preliminary Examination Rehearsal)
Summary:
NewsMe is a Personalized News Access System.
It allows users to provide feedbacks about theirs interests in news. Feedbacks are further used to construct the user model and recommend relevant news articles to users. It currently retrieves news from 82 RSS news feeds from 21 sources.
Discussion/Suggestion:
- the blacklist tracking graphs are odd. Because only certain people were really using the function. The specific case could be raised separately. It might due to that users were not aware of the blacklist function. however, telling users the capabilities of the functions should be done before the experiments.
- the precision on business topic is bit higher than other topics, why? why topic based?
- if user have better knowledge on the topic, did they do better? (it wasn't mentioned/analyzed in the study)
- implicit data is not reliable. the data is noisy. cos the question was asking for Japanese Car industry. people tend to recognize news title and may consider that news as relavant.
- describe the problem and technology>describe the study,what do you want to explore more>demo the system>explain the experiment
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