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Recommending collaboration with social networks: a comparative evaluation
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Proceedings of the SIGCHI conference on Human factors in computing systems table of contents
Ft. Lauderdale, Florida, USA
SESSION: Recommender systems and social computing table of contents
Pages: 593 - 600  
Year of Publication: 2003
ISBN:1-58113-630-7
Author
David W. McDonald  University of Washington, Seattle, Seattle, WA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 47,   Downloads (12 Months): 397,   Citation Count: 22
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ABSTRACT

Studies of information seeking and workplace collaboration often find that social relationships are a strong factor in determining who collaborates with whom. Social networks provide one means of visualizing existing and potential interaction in organizational settings. Groupware designers are using social networks to make systems more sensitive to social situations and guide users toward effective collaborations. Yet, the implications of embedding social networks in systems have not been systematically studied. This paper details an evaluation of two different social networks used in a system to recommend individuals for possible collaboration. The system matches people looking for expertise with individuals likely to have expertise. The effectiveness of social networks for matching individuals is evaluated and compared. One finding is that social networks embedded into systems do not match individuals' perceptions of their personal social network. This finding and others raise issues for the use of social networks in groupware. Based on the evaluation results, several design considerations are discussed.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY  22