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Author, Editor

Author(s):

Haghani, Parisa
Michel, Sebastian
Aberer, Karl

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Not MPG Author(s):

Haghani, Parisa
Aberer, Karl

Editor(s):

Huang, Xiangji Jimmy
Jones, Gareth
Koudas, Nick
Wu, Xindong
Collins-Thompson, Kevyn

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Not MPII Editor(s):

Huang, Xiangji Jimmy
Jones, Gareth
Koudas, Nick
Wu, Xindong
Collins-Thompson, Kevyn

BibTeX cite key*:

Haghani2010cikm

Title, Booktitle

Title*:

The Gist of Everything New: Personalized Top-k Processing over Web 2.0 Streams

Booktitle*:

Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM 2010)

Event, URLs

URL of the conference:

http://www.yorku.ca/cikm10/

URL for downloading the paper:

http://dx.acm.org/10.1145/1871437.1871502

Event Address*:

Toronto, Canada

Language:

English

Event Date*
(no longer used):


Organization:

Association for Computing Machinery (ACM)

Event Start Date:

26 October 2010

Event End Date:

30 October 2010

Publisher

Name*:

ACM

URL:


Address*:

New York, NY

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

489-498

Year*:

2010

VG Wort Pages:


ISBN/ISSN:

978-1-4503-0099-5

Sequence Number:


DOI:

10.1145/1871437.1871502



Note, Abstract, ©


(LaTeX) Abstract:

Web 2.0 portals have made content generation easier than ever with millions of users contributing news stories in form of posts in weblogs or short textual snippets as in Twitter. Efficient and effective filtering solutions are key to allow users stay tuned to this ever-growing ocean of information, releasing only relevant trickles of personal interest. In classical information filtering systems, user interests are formulated using standard IR techniques and data from all available information sources is filtered based on a predefined absolute quality-based threshold. In contrast to this restrictive approach which may still overwhelm the user with the returned stream of data, we envision a system which continuously keeps the user updated with only the top-$k$ relevant new information. Freshness of data is guaranteedby considering it valid for a particular time interval, controlled by a sliding window. Considering relevance as relative to the existing pool of new information creates a highly dynamic setting. We present POL-filter which together with our maintenance module constitute an efficient solution to this kind of problem. We show by comprehensive performance evaluations using real world data, obtained from a weblog crawl, that our approach brings performance gains compared to state-of-the-art.



Download
Access Level:

Institute

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{Haghani2010cikm,
AUTHOR = {Haghani, Parisa and Michel, Sebastian and Aberer, Karl},
EDITOR = {Huang, Xiangji Jimmy and Jones, Gareth and Koudas, Nick and Wu, Xindong and Collins-Thompson, Kevyn},
TITLE = {The Gist of Everything New: Personalized Top-k Processing over {Web} 2.0 Streams},
BOOKTITLE = {Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM 2010)},
PUBLISHER = {ACM},
YEAR = {2010},
ORGANIZATION = {Association for Computing Machinery (ACM)},
PAGES = {489--498},
ADDRESS = {Toronto, Canada},
ISBN = {978-1-4503-0099-5},
DOI = {10.1145/1871437.1871502},
}


Entry last modified by Anja Becker, 02/18/2011
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Editor(s)
[Library]
Created
02/07/2011 05:55:35 PM
Revision
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Editor
Anja Becker
Sebastian Michel


Edit Date
18.02.2011 12:05:31
02/07/2011 05:55:35 PM