de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Social Wisdom for Search and Recommendation

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44267

Crecelius,  Tom
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44377

Kacimi El Hassani,  Mouna
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons127842

Neumann,  Thomas
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45166

Parreira,  Josiane Xavier
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45528

Spaniol,  Marc
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Schenkel, R., Crecelius, T., Kacimi El Hassani, M., Neumann, T., Parreira, J. X., Spaniol, M., et al. (2008). Social Wisdom for Search and Recommendation. Bulletin of the Technical Committee of Data Engineering, 31(2), 40-49. Retrieved from http://sites.computer.org/debull/A08June/schenkel.pdf.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1D00-5
Abstract
Social-tagging communities offer great potential for smart recommendation and “socially enhanced” search result ranking. Beyond traditional forms of collaborative recommendation that are based on the item-user matrix of the entire community, a specific opportunity of social communities is to reflect the different degrees of friendships and mutual trust, in addition to the behavioral similarities among users. This paper presents a framework for harnessing such social relations for search and recommendation. The framework is implemented in the SENSE prototype system, and its usefulness is demonstrated in experiments with an excerpt of the librarything community data.