document: A personalized search engine based on web-snippet hierarchical clustering In this paper we propose a hierarchical clustering engine , called snaket , that is able to organize on-the-fly the search results drawn from 16 commodity search engines into a hierarchy of labeled folders . The hierarchy offers a complementary view to the flat-ranked list of results returned by current search engines . Users can navigate through the hierarchy driven by their search needs . This is especially useful for informative , polysemous and poor queries . SnakeT is the first complete and open-source system in the literature that offers both hierarchical clustering and folder labeling with variable-length sentences . We extensively test SnakeT against all available web-snippet clustering engines , and show that it achieves efficiency and efficacy performance close to the best known engine Vivisimo.com . Recently , personalized search engines have been introduced with the aim of improving search results by focusing on the users , rather than on their submitted queries . We show how to plug SnakeT on top of any ( un-personalized ) search engine in order to obtain a form of personalization that is fully adaptive , privacy preserving , scalable , and non intrusive for underlying search engines .
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