The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Shuffling a stacked deck: the case for partially randomized ranking of search engine results
VLDB '05 Proceedings of the 31st international conference on Very large data bases
KDD CUP-2005 report: facing a great challenge
ACM SIGKDD Explorations Newsletter
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Click-through prediction for news queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Search result re-ranking by feedback control adjustment for time-sensitive query
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Leveraging temporal dynamics of document content in relevance ranking
Proceedings of the third ACM international conference on Web search and data mining
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Exploring temporal evidence in web information retrieval
FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access
Detecting seasonal queries by time-series analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Expediting search trend detection via prediction of query counts
Proceedings of the sixth ACM international conference on Web search and data mining
Predicting event-relatedness of popular queries
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Recurrent event queries (REQ) constitute a special class of search queries occurring at regular, predictable time intervals. The freshness of documents ranked for such queries is generally of critical importance. REQ forms a significant volume, as much as 6% of query traffic received by search engines. In this work, we develop an improved REQ classifier that could provide significant improvements in addressing this problem. We analyze REQ queries, and develop novel features from multiple sources, and evaluate them using machine learning techniques. From historical query logs, we develop features utilizing query frequency, click information, and user intent dynamics within a search session. We also develop temporal features by time series analysis from query frequency. Other generated features include word matching with recurrent event seed words and time sensitivity of search result set. We use Naive Bayes, SVM and decision tree based logistic regression model to train REQ classifier. The results on test data show that our models outperformed baseline approach significantly. Experiments on a commercial Web search engine also show significant gains in overall relevance, and thus overall user experience.