WebIC: An Effective "Complete-Web" Recommender System
|Russ Greiner||University of Alberta||[Home Page]|
Notice: Hosted by Vinay Chaudhri.
Date: 2011-12-01 at 16:00
Location: EJ228 (SRI E building) (Directions)
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 in this sequence, 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 Haeubl and Bob Price
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