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  • × author_ss:"Etzioni, O."
  1. Shakes, J.; Langheinrich, M.; Etzioni, O.: Dynamic Reference Sifting : a case study in the homepage domain (1997) 0.00 
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    Abstract
    Presents Dynamic Reference Sifting - a novel architecture that attempts to provide both maximally comprehensive coverage and highly precise responses in real time, for specific home page categories. Describes Ahoy! The Homepage Finder (http://www.cs.washington.edu/research/ahoy), a fielded Web service that embodies Dynamic Reference Sifting for the domain of personal homepages. Ahoy! filters the output of mulptile Web indices to extract 1 or 2 references that are most likely to point to the person's homepage. If it finds no likely candidates, Ahoy! uses knowledge of homepage placement conventions, which it has accumulated from previous experience, to guess the URL for the desired homepage. Ahoy! finds the target homepage and ranks it as the top reference. 9% of the targets are found by guessing the URL. altaVista can find 58% of the targets and ranks only 23% of these as the top reference
    Date
    1. 8.1996 22:08:06
  2. Etzioni, O.; Weld, D.S.: Intelligent agents on the Internet : fact, fiction, and forecast (1995) 0.00 
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    Abstract
    Descusses intelligent information agents that are currently in use on the Internet. Describes the architecture and development approach used to provide scalable services to a wider range of users. Feedback from users is being used to refine and improve the intelligent agent service
  3. Zamir, O.; Etzioni, O.: Grouper : a dynamic clustering interface to Web search results (1999) 0.00 
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    Abstract
    Clustering is an effective way of organizing documents into collections for ease of browsing. Recently with the growth of WWW, clustering has become a paradigm for organizing search results. Online systems face many new challenges, including the need for fast response time, generating high quality clusters with simple descriptions for novice users, and working with document distributions that violates many traditional assumptions. How do different clustering algorithms trade off quality of clusters and speed? What modifications are necessary to adapt traditional clustering algorithm to the WWW? How do these system scale to larger document collection? How do these systems evaluate the quality of the cluster they generate? How are the clusters generated in each case, and are there any processing after cluster generation to improve on the cluster quality?

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