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Choosing the right crowd : expert finding in social networks. / Bozzon, Alessandro; Brambilla, Marco; Ceri, Stefano; Silvestri, Matteo; Vesci, Giuliano.

EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology. Association for Computing Machinery (ACM), 2013. p. 637-648.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Bozzon, A, Brambilla, M, Ceri, S, Silvestri, M & Vesci, G 2013, Choosing the right crowd: expert finding in social networks. in EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology. Association for Computing Machinery (ACM), pp. 637-648.

APA

Bozzon, A., Brambilla, M., Ceri, S., Silvestri, M., & Vesci, G. (2013). Choosing the right crowd: expert finding in social networks. In EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology (pp. 637-648). Association for Computing Machinery (ACM).

Vancouver

Bozzon A, Brambilla M, Ceri S, Silvestri M, Vesci G. Choosing the right crowd: expert finding in social networks. In EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology. Association for Computing Machinery (ACM). 2013. p. 637-648

Author

Bozzon, Alessandro ; Brambilla, Marco ; Ceri, Stefano ; Silvestri, Matteo ; Vesci, Giuliano. / Choosing the right crowd : expert finding in social networks. EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology. Association for Computing Machinery (ACM), 2013. pp. 637-648

BibTeX

@inproceedings{0257609a9b24417492a670e8a09f0d3a,
title = "Choosing the right crowd: expert finding in social networks",
abstract = "Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks.This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.",
author = "Alessandro Bozzon and Marco Brambilla and Stefano Ceri and Matteo Silvestri and Giuliano Vesci",
year = "2013",
language = "English",
isbn = "978-1-4503-1597-5",
pages = "637--648",
booktitle = "EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Choosing the right crowd

T2 - expert finding in social networks

AU - Bozzon, Alessandro

AU - Brambilla, Marco

AU - Ceri, Stefano

AU - Silvestri, Matteo

AU - Vesci, Giuliano

PY - 2013

Y1 - 2013

N2 - Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks.This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.

AB - Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks.This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.

M3 - Conference contribution

SN - 978-1-4503-1597-5

SP - 637

EP - 648

BT - EDBT '13 Proceedings of the 16th International Conference on Extending Database Technology

PB - Association for Computing Machinery (ACM)

ER -

ID: 15549748