document: Time-dependent semantic similarity measure of queries using historical click-through data It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines , which record the interactions between users and the search engines . Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor , while the click-through data is often dynamic and contains rich temporal information . In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data . The intuition is that more accurate semantic similarity values between queries can be obtained by taking into account the timestamps of the log data . With a set of user-defined calendar schema and calendar patterns , our time-dependent query similarity model is constructed using the marginalized kernel technique , which can exploit both explicit similarity and implicit semantics from the click-through data effectively . Experimental results on a large set of click-through data acquired from a commercial search engine show that our time-dependent query similarity model is more accurate than the existing approaches . Moreover , we observe that our time-dependent query similarity model can , to some extent , reflect real-world semantics such as real-world events that are happening over time .
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