Organizing User Search Histories

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...

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Main Authors: HWANG, Heasoo, LAUW, Hady W., GETOOR, Lise, NTOULAS, Alexandros
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/1548
https://ink.library.smu.edu.sg/context/sis_research/article/2547/viewcontent/tkde12a.pdf
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spelling sg-smu-ink.sis_research-25472017-12-26T05:53:04Z Organizing User Search Histories HWANG, Heasoo LAUW, Hady W. GETOOR, Lise NTOULAS, Alexandros 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. 2012-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1548 info:doi/10.1109/TKDE.2010.251 https://ink.library.smu.edu.sg/context/sis_research/article/2547/viewcontent/tkde12a.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University User history search history query clustering query reformulation click graph task identification. Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic User history
search history
query clustering
query reformulation
click graph
task identification.
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle User history
search history
query clustering
query reformulation
click graph
task identification.
Databases and Information Systems
Numerical Analysis and Scientific Computing
HWANG, Heasoo
LAUW, Hady W.
GETOOR, Lise
NTOULAS, Alexandros
Organizing User Search Histories
description 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.
format text
author HWANG, Heasoo
LAUW, Hady W.
GETOOR, Lise
NTOULAS, Alexandros
author_facet HWANG, Heasoo
LAUW, Hady W.
GETOOR, Lise
NTOULAS, Alexandros
author_sort HWANG, Heasoo
title Organizing User Search Histories
title_short Organizing User Search Histories
title_full Organizing User Search Histories
title_fullStr Organizing User Search Histories
title_full_unstemmed Organizing User Search Histories
title_sort organizing user search histories
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/1548
https://ink.library.smu.edu.sg/context/sis_research/article/2547/viewcontent/tkde12a.pdf
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