Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Why we search: visualizing and predicting user behavior
Proceedings of the 16th international conference on World Wide Web
Predicting the News of Tomorrow Using Patterns in Web Search Queries
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Time series analysis of a Web search engine transaction log
Information Processing and Management: an International Journal
Clustering and exploring search results using timeline constructions
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 11th ACM conference on Electronic commerce
Detecting periodic changes in search intentions in a search engine
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
Mining the web to predict future events
Proceedings of the sixth ACM international conference on Web search and data mining
Learning to predict from textual data
Journal of Artificial Intelligence Research
On mining mobile apps usage behavior for predicting apps usage in smartphones
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We present a hybrid method to turn off-the-shelf information retrieval (IR) systems into future event predictors. Given a query, a time series model is trained on the publication dates of the retrieved documents to capture trends and periodicity of the associated events. The periodicity of historic data is used to estimate a probabilistic model to predict future bursts. Finally, a hybrid model is obtained by intertwining the probabilistic and the time-series model. Our empirical results on the New York Times corpus show that autocorrelation functions of time-series suffice to classify queries accurately and that our hybrid models lead to more accurate future event predictions than baseline competitors.