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Organizing User Search Histories
May 2012 (vol. 24 no. 5)
pp. 912-925
ASCII Text
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Heasoo Hwang, Hady W. Lauw, Lise Getoor, Alexandros Ntoulas, "Organizing User Search Histories," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 5, pp. 912-925, May, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.251,
author = {Heasoo Hwang and Hady W. Lauw and Lise Getoor and Alexandros Ntoulas},
title = {Organizing User Search Histories},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {5},
issn = {1041-4347},
year = {2012},
pages = {912-925},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.251},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Organizing User Search Histories
IS - 5
SN - 1041-4347
SP912
EP925
EPD - 912-925
A1 - Heasoo Hwang,
A1 - Hady W. Lauw,
A1 - Lise Getoor,
A1 - Alexandros Ntoulas,
PY - 2012
KW - User history
KW - search history
KW - query clustering
KW - query reformulation
KW - click graph
KW - task identification.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Heasoo Hwang, Samsung Advanced Institute of Technology, Yongin-si
Hady W. Lauw, Institute for Infocomm Research, Singapore
Lise Getoor, University of Maryland, College Park
Alexandros Ntoulas, Microsoft Research, Mountain View
Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.

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Index Terms:
User history, search history, query clustering, query reformulation, click graph, task identification.
Citation:
Heasoo Hwang, Hady W. Lauw, Lise Getoor, Alexandros Ntoulas, "Organizing User Search Histories," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 5, pp. 912-925, May 2012, doi:10.1109/TKDE.2010.251
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