Time-dependent semantic similarity measure of queries using historical click-through data

• Main Authors: ZHAO, Qiankun, HOI, Steven C. H., LIU, Tie-Yan, BHOWMICK, Sourav S., LYU, Michael R., MA, Wei-Ying
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: click-through data; semantic similarity measure; marginalizedkernel; event detection; evolution pattern; Computer Sciences; Databases and Information Systems
• Online Access: 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
• Institution: Singapore Management University

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.