Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 6th international conference on Intelligent user interfaces
Optimizing search by showing results in context
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Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
The Reactive Keyboard
Thematic mapping - from unstructured documents to taxonomies
Proceedings of the eleventh international conference on Information and knowledge management
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A practical web-based approach to generating topic hierarchy for text segments
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
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
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Query suggestion using hitting time
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
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Efficient type-ahead search on relational data: a TASTIER approach
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Enhancing cluster labeling using wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Improving similarity measures for short segments of text
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
From "Dango" to "Japanese Cakes": Query Reformulation Models and Patterns
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
Topic based query suggestions for video search
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Analyzing, Detecting, and Exploiting Sentiment in Web Queries
ACM Transactions on the Web (TWEB)
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All state-of-the-art web search engines implement an auto-completion mechanism - an assistive technology enabling users to effectively formulate their search queries by predicting the next characters or words that they are likely to type. Query completions (or suggestions) are typically mined from past user interactions with the search engine, e.g., from query logs, clickthrough patterns, or query reformulations; they are ranked by some measure of query popularity, e.g., query frequency or clickthrough rate. Current query suggestion tools largely assume that the set of suggestions provided to the users is homogeneous, corresponding to a single real-world interpretation of the query. In this paper, we hypothesize that, in some cases, users would benefit from an alternative presentation of the suggestions, one where suggestions are not only ordered by likelihood but also organized by high-level user intent. Rich search suggestion interaction frameworks that reduce the user effort in identifying the set of relevant suggestions open new and promising directions towards improving user experience. Along these lines, we propose clustering the set of suggestions presented to a search engine user, and assigning an appropriate label to each subset of suggestions to help users quickly identify useful ones. For this, we present a variety of unsupervised clustering techniques for search suggestions, based on the information available to a large-scale web search engine. We evaluate our novel search suggestion presentation techniques on a real-world dataset of query logs. Based on a set of user studies, we show that by extending the existing assistance layer to effectively group suggestions and label them - while accounting for the query popularity - we substantially increase the user's satisfaction.