Proceedings Article, Paper
@InProceedings
Beitrag in Tagungsband, Workshop


Show entries of:

this year (2019) | last year (2018) | two years ago (2017) | Notes URL

Action:

login to update

Options:








Author, Editor

Author(s):

Luxenburger, Julia
Elbassuoni, Shady
Weikum, Gerhard

dblp
dblp
dblp



Editor(s):

Myaeng, Sung-Hyon
Oard, Douglas W.
Sebastiani, Fabrizio
Chua, Tat-Seng
Leong, Mun-Kew

dblp
dblp
dblp
dblp
dblp

Not MPII Editor(s):

Myaeng, Sung-Hyon
Oard, Douglas W.
Sebastiani, Fabrizio
Chua, Tat-Seng
Leong, Mun-Kew

BibTeX cite key*:

LuxSigir2008

Title, Booktitle

Title*:

Task-aware Search Personalization

Booktitle*:

31st Annual International ACM SIGIR Conference (SIGIR 2008)

Event, URLs

URL of the conference:

http://www.sigir.org/

URL for downloading the paper:

http://portal.acm.org/ft_gateway.cfm?id=1390469&type=pdf&coll=GUIDE&dl=GUIDE&CFID=11409746&CFTOKEN=47155444

Event Address*:

Singapore

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

20 July 2008

Event End Date:

24 July 2008

Publisher

Name*:

ACM

URL:

http://www.acm.org/

Address*:

New York, NY, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:

July

Pages:

721-722

Year*:

2008

VG Wort Pages:


ISBN/ISSN:

978-1-60558-164-4

Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

Search personalization has been pursued in many ways, in order to
provide better result rankings and better overall search experience
to individual users.
However, blindly applying personalization to all user queries, for example,
by a background model derived from the user's long-term query-and-click
history, is not always appropriate for aiding the user in accomplishing her actual task.
User interests change over time, a user sometimes works on very different categories of tasks
within a short timespan, and history-based personalization
may impede a user's desire of discovering new topics.
In this paper we propose a personalization framework that is
selective in a twofold sense. First, it selectively employs
personalization techniques for queries that are expected to benefit from prior history
information, while refraining from undue actions otherwise.
Second, we introduce the notion of tasks representing
different granularity levels of a user profile, ranging from very
specific search goals to broad topics, and base our reasoning selectively
on query-relevant user tasks.
These considerations are cast into a statistical language model for tasks, queries, and
documents, supporting both judicious query expansion and result re-ranking.
The effectiveness of our method is demonstrated by an empirical user study.



Download
Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{LuxSigir2008,
AUTHOR = {Luxenburger, Julia and Elbassuoni, Shady and Weikum, Gerhard},
EDITOR = {Myaeng, Sung-Hyon and Oard, Douglas W. and Sebastiani, Fabrizio and Chua, Tat-Seng and Leong, Mun-Kew},
TITLE = {Task-aware Search Personalization},
BOOKTITLE = {31st Annual International ACM SIGIR Conference (SIGIR 2008)},
PUBLISHER = {ACM},
YEAR = {2008},
PAGES = {721--722},
ADDRESS = {Singapore},
MONTH = {July},
ISBN = {978-1-60558-164-4},
}


Entry last modified by Ralf Schenkel, 12/23/2009
Show details for Edit History (please click the blue arrow to see the details)Edit History (please click the blue arrow to see the details)
Hide details for Edit History (please click the blue arrow to see the details)Edit History (please click the blue arrow to see the details)

Editor(s)
Julia Luxenburger
Created
04/07/2008 05:12:15 PM
Revisions
4.
3.
2.
1.
0.
Editor(s)
Ralf Schenkel
Martin Theobald
Olha Condor
Julia Luxenburger
Julia Luxenburger
Edit Dates
23.12.2009 15:50:02
04/20/2009 02:31:20 PM
18.11.2008 11:44:57
07/04/2008 17:15:15
07/04/2008 17:12:15