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

  • Authors:

  • Qiankun Zhao;Steven C. H. Hoi;Tie-Yan Liu;Sourav S. Bhowmick;Michael R. Lyu;Wei-Ying Ma

  • Affiliations:

  • Nanyang Technological University, Singapore;The Chinese University of HK, Hong Kong, China;Microsoft Research Asia, Beijing, China;Nanyang Technological University, Singapore;The Chinese University of HK, Hong Kong, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 15th international conference on World Wide Web
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.