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

Haghani, P., Michel, S., & Aberer, K. (2010). The Gist of Everything New: Personalized Top-k Processing over Web 2.0 Streams. In X. J. Huang, G. Jones, N. Koudas, X. Wu, & K. Collins-Thompson (Eds.), Proceedings of the 19th ACM Conference on Information and Knowledge Management (pp. 489-498). New York, NY: ACM. doi:10.1145/1871437.1871502.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-153D-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-3B17-D
Genre: Conference Paper

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 Creators:
Haghani, Parisa1, Author
Michel, Sebastian2, Author           
Aberer, Karl1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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 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.

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Language(s): eng - English
 Dates: 20102010
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 536401
DOI: 10.1145/1871437.1871502
URI: http://dx.acm.org/10.1145/1871437.1871502
Other: Local-ID: C1256DBF005F876D-EBA343F34E8A7C86C1257830005CFB08-Haghani2010cikm
 Degree: -

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Title: 19th ACM Conference on Information and Knowledge Management
Place of Event: Toronto, Canada
Start-/End Date: 2010-10-26 - 2010-10-30

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Title: Proceedings of the 19th ACM Conference on Information and Knowledge Management
  Abbreviation : CIKM 2010
Source Genre: Proceedings
 Creator(s):
Huang, Xiangji Jimmy1, Editor
Jones, Gareth1, Editor
Koudas, Nick1, Editor
Wu, Xindong1, Editor
Collins-Thompson, Kevyn1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 489 - 498 Identifier: ISBN: 978-1-4503-0099-5