Deterministic annealing EM algorithm
Neural Networks
Identifying ambiguous queries in web search
Proceedings of the 16th international conference on World Wide Web
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Proceedings of the 18th international conference on World wide web
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
Context-sensitive query auto-completion
Proceedings of the 20th international conference on World wide web
Post-ranking query suggestion by diversifying search results
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Context-aware query recommendation by learning high-order relation in query logs
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Personalized diversification of search results
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to personalize query auto-completion
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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The query suggestion or auto-completion mechanisms help users to type less while interacting with a search engine. A basic approach that ranks suggestions according to their frequency in query logs is suboptimal. Firstly, many candidate queries with the same prefix can be removed as redundant. Secondly, the suggestions can also be personalised based on the user's context. These two directions to improve the mechanisms' quality can be in opposition: while the latter aims to promote suggestions that address search intents that a user is likely to have, the former aims to diversify the suggestions to cover as many intents as possible. We introduce a contextualisation framework that utilises a short-term context using the user's behaviour within the current search session, such as the previous query, the documents examined, and the candidate query suggestions that the user has discarded. This short-term context is used to contextualise and diversify the ranking of query suggestions, by modelling the user's information need as a mixture of intent-specific user models. The evaluation is performed offline on a set of approximately 1.0M test user sessions. Our results suggest that the proposed approach significantly improves query suggestions compared to the baseline approach.