BibSonomyburst/user/beate/click-throughBibSonomy RSS feed for /user/beate/click-through2012-02-16T20:52:50+01:00http://www.bibsonomy.org/bibtex/257cbc64550d3a1b5b8599a0783e95111/beatebeate2007-10-17T14:15:02+02:00click-through ir kernel time <span class="authorEditorList"><a href="/author/Zhao">Qiankun Zhao</a>, <a href="/author/Hoi">Steven C. H. Hoi</a>, <a href="/author/Liu">Tie-Yan Liu</a>, <a href="/author/Bhowmick">Sourav S. Bhowmick</a>, <a href="/author/Lyu">Michael R. Lyu</a>, and <a href="/author/Ma">Wei-Ying Ma</a> </span><em>WWW &#039;06: Proceedings of the 15th international conference on World Wide Web, </em><em>page 543--552. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2006</em>)Wed Oct 17 14:15:02 CEST 2007New York, NY, USAWWW '06: Proceedings of the 15th international conference on World Wide Web543--552Time-dependent semantic similarity measure of queries using historical click-through data2006click-through ir kernel time 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.Time-dependent semantic similarity measure of queries using historical click-through data