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  Making SENSE: Socially Enhanced Search and Exploration

Crecelius, T., Kacimi El Hassani, M., Michel, S., Neumann, T., Parreira, J. X., Schenkel, R., et al. (2008). Making SENSE: Socially Enhanced Search and Exploration. In P. Buneman, B. C. Ooi, K. Ross, & G. Weber (Eds.), Proceedings of the 34th International Conference on Very Large Data Bases (VLDB 2008) (pp. 1480-1483). New York, NY: ACM.

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 Creators:
Crecelius, Tom1, 2, Author           
Kacimi El Hassani, Mouna1, Author           
Michel, Sebastian1, Author           
Neumann, Thomas1, Author           
Parreira, Josiane Xavier1, Author           
Schenkel, Ralf1, 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 like Flickr, del.icio.us and YouTube have established themselves as very popular and powerful services for publishing and searching contents, but also for identifying other users who share similar interests. In these communities, data is usually annotated with carefully selected and often semantically meaningful tags, collaboratively chosen by the user who uploaded an item and other users who came across the item. Items like urls or videos are typically retrieved by issueing queries that consist of a set of tags, returning items that have been frequently annotated with these tags. However, users often prefer a more personalized way of searching over such a ‘global’ search, exploiting preferences of and connections between users. The SENSE system presented in this demo supports hybrid personalization along two dimensions: in the social dimension, a search process is focused towards items tagged by users explicitly selected as friends by the querying user, whereas in the spiritual dimension, users that share preferences with the querying user are preferred. Orthorgonal to this, the system additionally integrates semantic expansion of query tags to improve search results. SENSE provides an efficient top-k algorithm that dynamically expands the search to related users and tags. It is based on principles of threshold algorithms, folding related users and tags into the search space in an incremental on-demand manner, thus visiting only a small fraction of the social network when evaluating a query. The demonstration uses three different real-world datasets: A large set of urls from del.icio.us, a large set of pictures from Flickr, and a large set of books from librarything, each together with a large fraction of the corresponding social network of these sites.

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Language(s): eng - English
 Dates: 2009-03-232008
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 428019
DOI: 10.1145/1454159.1454206
URI: http://doi.acm.org/10.1145/1454159.1454206
Other: Local-ID: C125756E0038A185-50096C0B16F09DF5C12574550040BA99-CreceliusKMNPSW_VLDB08
 Degree: -

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Title: Untitled Event
Place of Event: Auckland, New Zealand
Start-/End Date: 2008-08-24 - 2008-08-30

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Source 1

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Title: Proceedings of the 34th International Conference on Very Large Data Bases (VLDB 2008)
Source Genre: Proceedings
 Creator(s):
Buneman, Peter, Editor
Ooi, Beng Chin, Editor
Ross, Kenneth, Editor
Weber, Gerald, Editor
Affiliations:
-
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1480 - 1483 Identifier: ISBN: 978-1-60558-305-1

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Title: Proceedings of the VLDB Endowment
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -