Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
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
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Type less, find more: fast autocompletion search with a succinct index
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Examining the effectiveness of real-time query expansion
Information Processing and Management: an International Journal
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
Efficient interactive query expansion with complete search
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Introduction to Information Retrieval
Introduction to Information Retrieval
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
Search advertising using web relevance feedback
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Proceedings of the 18th international conference on World wide web
Efficient interactive fuzzy keyword search
Proceedings of the 18th international conference on World wide web
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
Web Query Recommendation via Sequential Query Prediction
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Large scale query log analysis of re-finding
Proceedings of the third ACM international conference on Web search and data mining
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
Effectively interpreting keyword queries on RDF databases with a rear view
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
A noise-aware click model for web search
Proceedings of the fifth ACM international conference on Web search and data mining
SWiPE: searching wikipedia by example
Proceedings of the 21st international conference companion on World Wide Web
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
COMMA: A Result-Oriented Composite Autocompletion Method for E-marketplaces
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
User model-based metrics for offline query suggestion evaluation
Proceedings of the 36th 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
Time-aware structured query suggestion
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Behavioral dynamics on the web: Learning, modeling, and prediction
ACM Transactions on Information Systems (TOIS)
Intent models for contextualising and diversifying query suggestions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Analyzing, Detecting, and Exploiting Sentiment in Web Queries
ACM Transactions on the Web (TWEB)
Efficient error-tolerant query autocompletion
Proceedings of the VLDB Endowment
Proceedings of the 18th Australasian Document Computing Symposium
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
Web Intelligence and Agent Systems
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Query auto completion is known to provide poor predictions of the user's query when her input prefix is very short (e.g., one or two characters). In this paper we show that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes. We propose a context-sensitive query auto completion algorithm, NearestCompletion, which outputs the completions of the user's input that are most similar to the context queries. To measure similarity, we represent queries and contexts as high-dimensional term-weighted vectors and resort to cosine similarity. The mapping from queries to vectors is done through a new query expansion technique that we introduce, which expands a query by traversing the query recommendation tree rooted at the query. In order to evaluate our approach, we performed extensive experimentation over the public AOL query log. We demonstrate that when the recent user's queries are relevant to the current query she is typing, then after typing a single character, NearestCompletion's MRR is 48% higher relative to the MRR of the standard MostPopularCompletion algorithm on average. When the context is irrelevant, however, NearestCompletion's MRR is essentially zero. To mitigate this problem, we propose HybridCompletion, which is a hybrid of NearestCompletion with MostPopularCompletion. HybridCompletion is shown to dominate both NearestCompletion and MostPopularCompletion, achieving a total improvement of 31.5% in MRR relative to MostPopularCompletion on average.