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Web Discovery and Filtering Based on Textual Relevance Feedback Learning
Author(s) -
Lam Wai,
Wang Wei,
Yue CheWang
Publication year - 2003
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/1467-8640.00217
Subject(s) - computer science , information retrieval , hyperlink , relevance feedback , web page , relevance (law) , world wide web , context (archaeology) , artificial intelligence , image retrieval , paleontology , political science , law , image (mathematics) , biology
We develop a new approach for Web information discovery and filtering. Our system, called WID, allows the user to specify long‐term information needs by means of various topic profile specifications. An entire example page or an index page can be accepted as input for the discovery. It makes use of a simulated annealing algorithm to automatically explore new Web pages. Simulated annealing algorithms possess some favorable properties to fulfill the discovery objectives. Information retrieval techniques are adopted to evaluate the content‐based relevance of each page being explored. The hyperlink information, in addition to the textual context, is considered in the relevance score evaluation of a Web page. WID allows users to provide three forms of the relevance feedback model, namely, the positive page feedback, the negative page feedback, and the positive keyword feedback. The system is domain independent and does not rely on any prior knowledge or information about the Web content. Extensive experiments have been conducted to demonstrate the effectiveness of the discovery performance achieved by WID.