Term extraction for user profiling: Evaluation by the user

We compared three term scoring methods in their ability to extract descriptive terms from a knowledge worker's document collection. We compared the methods in two different evaluation scenarios, both from the perspective of the user: a perterm evaluation, and a holistic (term cloud) evaluation. We found that users tend to prefer a term scoring method that gives a higher score to multi-word terms than to single-word terms. In addition, users are not always consistent in their judgements of term profiles, if they are presented in different forms (as list or as cloud).