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 similarit...
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| Main Authors: | , , , , , |
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| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2006
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| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/2391 https://ink.library.smu.edu.sg/context/sis_research/article/3391/viewcontent/ClickModel_WWW_06.pdf |
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| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | 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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