A large-scale study of the evolution of web pages
WWW '03 Proceedings of the 12th international conference on World Wide Web
Sic transit gloria telae: towards an understanding of the web's decay
Proceedings of the 13th international conference on World Wide Web
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Web Dragons: Inside the Myths of Search Engine Technology
Web Dragons: Inside the Myths of Search Engine Technology
Deep classification in large-scale text hierarchies
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
WeBrowSearch: toward web browser with autonomous search
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Building a dynamic classifier for large text data collections
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Let's Trust Users It is Their Search
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Naive bayes for text classification with unbalanced classes
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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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 (context-dependent) way with a minimum of effort on their part.