Recurrent event queries (REQ) constitute a special class of search queries occurring at regular, predictable time intervals. The fresh- ness 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 classi- fier that could provide significant improve- ments in addressing this problem. We ana- lyze REQ queries, and develop novel features from multiple sources, and evaluate them us- ing machine learning techniques. From histor- ical 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 gen...

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