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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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 Creators:
Schenkel, Ralf1, Author           
Crecelius, Tom1, 2, Author           
Kacimi El Hassani, Mouna1, Author           
Michel, Sebastian1, Author           
Neumann, Thomas1, Author           
Parreira, Josiane Xavier1, Author           
Weikum, Gerhard1, Author           
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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 Abstract: 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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Language(s): eng - English
 Dates: 2009-03-262008
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 428213
Other: Local-ID: C125756E0038A185-379D646FF215A8AFC125742000389BCB-SchenkelCKMNPW08
 Degree: -

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Title: SIGIR 2008
Place of Event: Singapore, Singapore
Start-/End Date: 2008-07-20 - 2008-07-24

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Title: ACM SIGIR 2008 : Thirty-First Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Source Genre: Proceedings
 Creator(s):
Myaeng, Sung-Hyon, Editor
Oard, Douglas W., Editor
Sebastiani, Fabrizio, Editor
Chua, Tat-Seng, Editor
Leong, Mun-Kew, Editor
Affiliations:
-
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 523 - 530 Identifier: ISBN: 978-1-60558-164-4

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Title: ACM SIGIR Forum
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