A common, yet difficult, task for people is planning satisfactory
driving routes. Automatic systems for driving directions exist, but
one of their major difficulties is the range of individual difference
regarding what constitutes a good route and a bad route. We introduce
an adaptive user interface for route planning that uses relative
feedback to model individual route preferences. Our experiments test
three adaptive algorithms for relative preferences on two feature
sets. The results show that all algorithms perform similarly on the
training data, but the simpler algorithms have the better test
performance, probably because the more complex algorithms fit the
noise in the training data. We intend to improve the accuracy of our
adaptive algorithms by feature engineering and incorporating domain
knowledge about route preferences.
Date: Wed., April 22; Time: 4:15-5:30PM; Place: Cordura 100
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