Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2006
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.
Keywords
click-through data, semantic similarity measure, marginalizedkernel, event detection, evolution pattern
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
WWW '06: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, May 23-26
First Page
543
Last Page
552
ISBN
9781595933232
Identifier
10.1145/1135777.1135858
Publisher
ACM
City or Country
New York
Citation
ZHAO, Qiankun; HOI, Steven C. H.; LIU, Tie-Yan; BHOWMICK, Sourav S.; LYU, Michael R.; and MA, Wei-Ying.
Time-dependent semantic similarity measure of queries using historical click-through data. (2006). WWW '06: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, May 23-26. 543-552.
Available at: https://ink.library.smu.edu.sg/sis_research/2391
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1135777.1135858