Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Categorizing web queries according to geographical locality
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A temporal comparison of AltaVista Web searching: Research Articles
Journal of the American Society for Information Science and Technology
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automatic web query classification using labeled and unlabeled training data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Towards the Detection of Breaking News from Online Web Search Keywords
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Automatic classification of Web queries using very large unlabeled query logs
ACM Transactions on Information Systems (TOIS)
Detection of breaking news from online web search queries
New Generation Computing
Computational community interest for ranking
Proceedings of the 18th ACM conference on Information and knowledge management
Clustering queries for better document ranking
Proceedings of the 18th ACM conference on Information and knowledge management
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With the development of Web search engines, it is considered as an important task to provide retrieved documents in a proper manner. Many search engines have used various document ranking algorithms to provide their retrieved documents in a more efficient way for users. However, even though a good algorithm is used, there are some limitations if they do not consider the characteristic of queries which is diverse depending on user intention or interest. Even if a user searches documents with the same query, he/she may want a different result depending on when he/she queries into a search engine. How can a search engine judge what way is more efficient to provide retrieved results? We suggest a simple and novel way which employs query-related Web context to answer this question. With the distribution of query-related tweets and news articles, we classify whether a query would be considered as a hot query or a cold query. And then, we extract major topic terms from the hot time slice if a query is classified as a hot query, or extract refined contents if a query is classified as a cold query. Finally, all retrieved results are re-ranked by reflecting these topic terms or refined contents according to the characteristic of the query. To show the meaningfulness of our approach, we compare our re-ranked results with original retrieved results from the commercial search engine.