Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Proceedings of the 11th international conference on World Wide Web
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Effective and efficient user interaction for long queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in 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
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Refined experts: improving classification in large taxonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Entropy-biased models for query representation on the click graph
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Post-rank reordering: resolving preference misalignments between search engines and end users
Proceedings of the 18th ACM conference on Information and knowledge management
Query reformulation using anchor text
Proceedings of the third ACM international conference on Web search and data mining
An optimization framework for query recommendation
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
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
Metaphor: a system for related search recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
Utilizing query change for session search
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
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
When do people use query suggestion? A query suggestion log analysis
Information Retrieval
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Query suggestion is an interactive approach for search engines to better understand users information need. In this paper, we propose a novel query suggestion framework which leverages user re-query feedbacks from search engine logs. Specifically, we mined user query reformulation activities where the user only modifies part of the query by (1) adding terms after the query, (2) deleting terms within the query, or (3) modifying terms to new terms. We build a term-transition graph based on the mined data. Two models are proposed which address topic-level and term-level query suggestions, respectively. In the first topic-based unsupervised Pagerank model, we perform random walk on each of the topic-based term-transition graph and calculate the Pagerank for each term within a topic. Given a new query, we suggest relevant queries based on its topic distribution and term-transition probability within each topic. Our second model resembles the supervised learning-to-rank (LTR) framework, in which term modifications are treated as documents so that each query reformulation is treated as a training instance. A rich set of features are constructed for each (query, document) pair from Pagerank, Wikipedia, N-gram, ODP and so on. This supervised model is capable of suggesting new queries on a term level which addresses the limitation of previous methods. Experiments are conducted on a large data set from a commercial search engine. By comparing the with state-of-the-art query suggestion methods [4, 2], our proposals exhibit significant performance increase for all categories of queries.