Faculty Host: Michael Littman

Abstract:

Many web recommendation systems direct users to webpages, from a single website, that other similar users have visited. By contrast, our WebIC web recommendation system is designed to locate "information content (IC) pages" --- pages the current user needs to see to complete her task --- from essentially anywhere on the web. WebIC first extracts the "browsing properties" of each word encountered in the user's current click-stream --- eg, how often each word appears in the title of a page, or in the "anchor" of a link that was followed, etc. It then uses a user- and site-independent model, learned from a set of annotated web logs acquired in a user study, to determine which of these words is likely to appear in an IC page. We discuss how to use these IC-words to find IC-pages, and demonstrate empirically that this browsing-based approach works effectively. Joint work with Tingshao Zhu, Gerald H„ubl and Bob Price For more information, click here.

Speaker Bio:

After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science and the founding Director of the Alberta Ingenuity Centre for Machine Learning. He is a Program Chair for the 2004 "Int'l Conference on Machine Learning", an Editor-in-Chief for "Computational Intelligence", and serves on the editorial boards of a number of other journals. He has published over 90 refereed papers and patents, most in the areas of machine learning and knowledge representation. The main foci of his current work are (1) bioinformatics and medical informatics; (2) learning effective probabilistic models and (3) formal foundations of learnability.

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