Title

Time-Dependent Semantic Similarity Measure of Queries Using Historical Click-Through Data

Publication Type

Conference Proceeding Article

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 Management and Analytics

Publication

Proceedings of the 15th International Conference on World Wide Web: Edinburgh, Scotland, May 23-26, 2006

First Page

543

Last Page

552

ISBN

9781595933232

Identifier

10.1145/1135777.1135858

Publisher

ACM

City or Country

New York

Additional URL

http://dx.doi.org/10.1145/1135777.1135858