Fast density estimation using CF-kernel for very large databases
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Determining the user intent of web search engine queries
Proceedings of the 16th international conference on World Wide Web
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
An optimization framework for query recommendation
Proceedings of the third ACM international conference on Web search and data mining
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
QAque: faceted query expansion techniques for exploratory search using community QA resources
Proceedings of the 21st international conference companion on World Wide Web
Journal of Information Science
Personalized Query Expansion for Web Search Using Social Keywords
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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Many of today's search engines provide autocompletion while the user is typing a query string. This type of dynamic query suggestion can help users to formulate queries that better represent their search intent during Web search interactions. In this paper, we demonstrate our query suggestion system called CONQUER, which allows to efficiently suggest queries for a given partial query and a number of available query context observations. The context-awareness allows for suggesting queries tailored to a given context, e.g., the user location or the time of day. CONQUER uses a suggestion model that is based on the combined probabilities of sequential query patterns and context observations. For this, the weight of a context in a query suggestion can be adjusted online, for example, based on the learned user behavior or user profiles. We demonstrate the functionality of CONQUER based on 6 million queries from an AOL query log using the time of day and the country domain of the clicked URLs in the search result as context observations.