Personalized Meta Search Engine
Amoon, Mohammed . 2010
Users of a web search engine are often forced to shift through the long ordered list of document "snippet‖ which returned by the search engine. The majority of today’s web search engines (e.g., Google, Yahoo, and AltaVista) follow this paradigm. The law precision of web search engines coupled with the ranked list presentation make it hard for users to find the information they are looking for .Nowadays, the information retrieval community has explored many alternative methods for organizing the retrieval results and makes the search engine results easy to browse. Beside this, it is also try to focus on the user needs rather than the user query; this is called a personalization technique. This would be satisfied by assigning a profile for each user or learning from his past behaviors or clustering the returned result into groups and allow the user to choose the label (or labels) that fit his needs in the current time . In this paper, a Personalized Meta Search Engine (PMSE) has been proposed based on the clustering approach which queries three famous search engines (Google, Yahoo, and msn) and offers two complementary views on their returned flat results. The first view is the classical ranked list and the other consists of a hierarchical organization of results into labeled clusters created on-the-fly at the query time. Users can browse the hierarchy with various techniques such as knowledge extraction, query refinement, and personalization of search results. These techniques organize numerous search results into several meaningful categories (clusters). The proposed PMSE has been supported with an efficient user interface which allows the user to choose his favor search engines and the number of results from each one. Also, PMSE is supported with an efficient result page which provides the user the ability of selecting more than one cluster. A comparative study between clustering search results using snippets returned from search engines and clustering search results using the original documents has been introduced. Also, the proposed PMSE has been compared against the best known clustering engine, namely Yippy.com.
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