Search (2 results, page 1 of 1)

  • × author_ss:"Tombros, A."
  • × year_i:[2000 TO 2010}
  1. Tombros, A.; Villa, R.; Rijsbergen, C.J. Van: ¬The effectiveness of query-specific hierarchic clustering in information retrieval (2002) 0.00 
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    Abstract
    Hierarchic document clustering has been widely applied to information retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search (IFS). However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view that if hierarchic clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional IFS. We conducted a number of experiments using five document collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering is indeed higher, and suggest that there is scope for its application to IR.
    Type
    a
  2. Tombros, A.; Ruthven, I.; Jose, J.M.: How users assess Web pages for information seeking (2005) 0.00 
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    Abstract
    In this article, we investigate the criteria used by online searchers when assessing the relevance of Web pages for information-seeking tasks. Twenty-four participants were given three tasks each, and they indicated the Features of Web pages that they used when deciding about the usefulness of the pages in relation to the tasks. These tasks were presented within the context of a simulated work-task situation. We investigated the relative utility of features identified by participants (Web page content, structure, and quality) and how the importance of these features is affected by the type of information-seeking task performed and the stage of the search. The results of this study provide a set of criteria used by searchers to decide about the utility of Web pages for different types of tasks. Such criteria can have implications for the design of systems that use or recommend Web pages.
    Type
    a

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