WebWatcher: A Learning Apprentice for the World Wide Web Robert Armstrong, Dayne Freitag, Thorsten Joachims and Tom Mitchell 발표자 : 자연언어처리연구실 김정집

2 Overview WebWatcher  information seeking assistant for the WWWWebWatcherUser WWW Advice Tracking user’s search Prefetch MosaicMosaic

3 Typical scenario 1. User sets up WebWatcher for his/her goal. 2. WebWatcher now “looking over his shoulder” and recommend related link by its knowledge. 3. User decides to select this advice or not. 4. Until the user dismiss WebWatcher continue step 2~4. User remains firmly in control.

4 Typical scenario

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7 Learning for guiding search(1/2) What Should be Learned?  Simple learning function  UserChoice?:Page Goal Link->[0,1] –the value is the probability that an arbitrary user will select Link given the current Page and Goal How Should Pages, Links and Goals be Represented?  530 boolean features

8 Learning for guiding search(2/2) What Learning Method Should be Used?  Winnow  Wordstat  TF/IDF with cosine similarity measure  Random  to provide baseline measure

9 Results(1/2) 30 sessions using WebWatcher to search for technical papers How Accurately Can UserChoice? be Learned?

10 Results(2/2) Can Accuracy be Improved by Sacrificing Coverage?

11 Conclusions Software assistance is needed. The accuracy of the agent’s advice can be increased by allowing it to give advice only when it has high confidence. We are optimistic that a learning apprentice for the WWW is feasible.