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  Efficient Top-k Querying over Social-Tagging Networks

Schenkel, R., Crecelius, T., Kacimi El Hassani, M., Michel, S., Neumann, T., Parreira, J. X., et al. (2008). Efficient Top-k Querying over Social-Tagging Networks. In S.-H. Myaeng, D. W. Oard, F. Sebastiani, T.-S. Chua, & M.-K. Leong (Eds.), ACM SIGIR 2008: Thirty-First Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 523-530). New York, NY: ACM.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-1B81-F Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0015-7E6A-6
Genre: Konferenzbeitrag

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 Urheber:
Schenkel, Ralf1, Autor           
Crecelius, Tom1, 2, Autor           
Kacimi El Hassani, Mouna1, Autor           
Michel, Sebastian1, Autor           
Neumann, Thomas1, Autor           
Parreira, Josiane Xavier1, Autor           
Weikum, Gerhard1, Autor           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, ou_1116551              

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Schlagwörter: -
 Zusammenfassung: Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances. Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio.us, Flickr, and LibraryThing.

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Sprache(n): eng - English
 Datum: 2009-03-262008
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 428213
Anderer: Local-ID: C125756E0038A185-379D646FF215A8AFC125742000389BCB-SchenkelCKMNPW08
 Art des Abschluß: -

Veranstaltung

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Titel: SIGIR 2008
Veranstaltungsort: Singapore, Singapore
Start-/Enddatum: 2008-07-20 - 2008-07-24

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Quelle 1

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Titel: ACM SIGIR 2008 : Thirty-First Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Genre der Quelle: Konferenzband
 Urheber:
Myaeng, Sung-Hyon, Herausgeber
Oard, Douglas W., Herausgeber
Sebastiani, Fabrizio, Herausgeber
Chua, Tat-Seng, Herausgeber
Leong, Mun-Kew, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: New York, NY : ACM
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 523 - 530 Identifikator: ISBN: 978-1-60558-164-4

Quelle 2

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Titel: ACM SIGIR Forum
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: -