The Journal of Machine Learning Research
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Examining the effectiveness of real-time query expansion
Information Processing and Management: an International Journal
Defining a session on Web search engines: Research Articles
Journal of the American Society for Information Science and Technology
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Efficient interactive fuzzy keyword search
Proceedings of the 18th international conference on World wide web
Extending autocompletion to tolerate errors
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Personalized click prediction in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Optimal rare query suggestion with implicit user feedback
Proceedings of the 19th international conference on World wide web
Suggesting Topic-Based Query Terms as You Type
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The demographics of web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Personalizing web search using long term browsing history
Proceedings of the fourth ACM international conference on Web search and data mining
Understanding and predicting personal navigation
Proceedings of the fourth ACM international conference on Web search and data mining
Context-sensitive query auto-completion
Proceedings of the 20th international conference on World wide web
Inferring and using location metadata to personalize web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Query suggestions in the absence of query logs
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Post-ranking query suggestion by diversifying search results
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the 20th ACM international conference on Information and knowledge management
Personalizing web search results by reading level
Proceedings of the 20th ACM international conference on Information and knowledge management
Query suggestion by constructing term-transition graphs
Proceedings of the fifth ACM international conference on Web search and data mining
Learning to complete sentences
ECML'05 Proceedings of the 16th European conference on Machine Learning
Actualization of query suggestions using query logs
Proceedings of the 21st international conference companion on World Wide Web
Adaptive query suggestion for difficult queries
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to suggest: a machine learning framework for ranking query suggestions
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Modeling the impact of short- and long-term behavior on search personalization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Metaphor: a system for related search recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
Demographic context in web search re-ranking
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning to rank query suggestions for adhoc and diversity search
Information Retrieval
Intent models for contextualising and diversifying query suggestions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 18th Australasian Document Computing Symposium
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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Query auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes tend to be short and ambiguous, and existing models mostly rely on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary drastically across different demographics and users. For instance, while instagram and imdb have comparable popularities overall and are both legitimate candidates to show for prefix i, the former is noticeably more popular among young female users, and the latter is more likely to be issued by men. In this paper, we present a supervised framework for personalizing auto-completion ranking. We introduce a novel labelling strategy for generating offline training labels that can be used for learning personalized rankers. We compare the effectiveness of several user-specific and demographic-based features and show that among them, the user's long-term search history and location are the most effective for personalizing auto-completion rankers. We perform our experiments on the publicly available AOL query logs, and also on the larger-scale logs of Bing. The results suggest that supervised rankers enhanced by personalization features can significantly outperform the existing popularity-based base-lines, in terms of mean reciprocal rank (MRR) by up to 9%.