ACM Transactions on Information Systems (TOIS)
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning recurrent event queries for web search
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
Identification of top relevant temporal expressions in documents
Proceedings of the 2nd Temporal Web Analytics Workshop
Modeling and predicting behavioral dynamics on the web
Proceedings of the 21st international conference 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
Learning to rank search results for time-sensitive queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Expediting search trend detection via prediction of query counts
Proceedings of the sixth ACM international conference on Web search and data mining
Fast data in the era of big data: Twitter's real-time related query suggestion architecture
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A survey of temporal web search experience
Proceedings of the 22nd international conference on World Wide Web companion
Behavioral dynamics on the web: Learning, modeling, and prediction
ACM Transactions on Information Systems (TOIS)
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often reflected in search logs in form of seasonal spikes in the frequency of related queries (e.g. "halloween costumes", "where is santa"). Many seasonal queries such as "sigir conference" mainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g.sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.