Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Modern Information Retrieval
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Proceedings of the 13th international conference on World Wide Web
Query Mining for Community Based Web Search
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Understanding the relationship between searchers' queries and information goals
Proceedings of the 17th ACM conference on Information and knowledge management
Intentional query suggestion: making user goals more explicit during search
Proceedings of the 2009 workshop on Web Search Click Data
Dependency relation based detection of lexicalized user goals
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Modeling and analysis of cross-session search tasks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Search, interrupted: understanding and predicting search task continuation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Mining subtopics from different aspects for diversifying search results
Information Retrieval
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With the fast growth of the Web, users often suffer from the problem of information overload since many existing search engines response lots of non-relevant documents containing query terms based on the search mechanism of keyword matching. In fact, it is eagerly expected by both users and search engine developers to reduce overloaded information by understanding user goals clearly. In this paper, we intend to utilize Web search results to identify user goals. We propose one novel probabilistic inference model which effectively employs syntactic features to discover a variety of confined user goals.