Abstract-The current search engine model considers users not trustworthy, so no tools are provided to let them specify what they are looking for or in what context, which severely limits what they are able to achieve. Instead, search engines try to guess that, which is currently done using "implicit feedback".In this paper we propose a "web exploration engine" -a model where users can use the search engine as their tool and explicitly specify the context of their search. Information about the web has been pre-classified in a large number of categories; users can explore this hierarchy by providing relevance feedback or search within a particular category. Search is truly "local" in the sense that keyword relevance is not global, but specific to that category. In contrast to the existing search engines, users can explore the web without any keywords, guiding the exploration engine with relevance feedback alone.

Abstract. A significant problem of the dominant web search model is the lack of a realistic way to acquire user search context. Search engines use implicit feedback, which is extremely sparse and does not allow users to properly define what they want to know, or what they think of search results. In our proposed "web exploration engine", which we implemented as a prototype, documents have been automatically pre-classified into a large number of categories representing a hierarchy of search contexts. Users can browse this structure or search within a particular category (context) by explicitly selecting it. Keyword relevance is not global but specific to a category. The main innovation we propose is the "floating" query resulting from this feature: the original search query is re-evaluated and the importance of its features re-calculated for every context the user explores. This allows users to search or browse in a truly local (contextdependent) way with a minimum of effort on their part.