Seasonal events such as Halloween and Christmas repeat ev-
ery year and initiate several temporal information needs.
The impact of such events on users is often re
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.
In Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval