Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Using terminological feedback for web search refinement: a log-based study
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Re-examining the potential effectiveness of interactive query expansion
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Query word deletion prediction
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Scoring missing terms in information retrieval tasks
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Generating query substitutions
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
Studying the use of popular destinations to enhance web search interaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Using the wisdom of the crowds for keyword generation
Proceedings of the 17th 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
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to rank query reformulations
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation for long-tail queries via templates
Proceedings of the 20th international conference on World wide web
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
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Search engine users are increasingly performing complex tasks based on the simple keyword-in document-out paradigm. To assist users in accomplishing their tasks effectively, search engines provide query recommendations based on the user's current query. These are suggestions for follow-up queries given the user-provided query. A large number of techniques have been proposed in the past on mining such query recommendations which include past user sessions (e.g., sequence of queries within a specified window of time) to identify most frequently occurring pairs, using click-through graphs (e.g., a bipartite graph of queries and the urls on which users clicked) and rank these suggestions using some form of frequency counts from the past query logs. Given the limited number of queries that are offered (typically 5) it is important to effectively rank them. In this paper, we present a novel approach to ranking query recommendations which not only consider relevance to the original query but also take into account efficiency of a query at accomplishing a user search task at hand. We formalize the notion of query efficiency and show how our objective function effectively captures this as determined by a human study and eliminates biases introduced by click-through based metrics. To compute this objective function, we present a pseudosupervised learning technique where no explicit human experts are required to label samples. In addition, our techniques effectively characterize preferred url destinations and project each query into a higher dimension space where each sub-spaces represents user intent using these characteristics. Finally, we present an extensive evaluation of our proposed methods against production systems and show our method to increase task completion efficiency by 15%.