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An answerer recommender system exploiting collaboration in CQA services

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Date
2016
Dewey
Informatique générale
Sujet
Recommender systems; Prototypes; Logistics; Knowledge management
DOI
http://dx.doi.org/10.1109/CSCWD.2016.7565988
Conference name
International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Conference date
05-2016
Conference city
Nanchang
Conference country
China
Book title
2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Author
Shen, Weiming; Liu, Xiaoping; Yang, Chunsheng; Barthès, Jean-Paul; Luo, Junzhou; Chen, Limin; Yong, Jianming
Publisher
Institute of Electrical and Electronics Engineers ( IEEE )
Year
09-2016
Pages number
703
ISBN
978-1-5090-1915-1
URI
https://basepub.dauphine.fr/handle/123456789/16568
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Wang, Ning
389870 Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Abel, Marie-Hélène
389870 Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Barthès, Jean-Paul
389870 Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Negre, Elsa
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
198-203
Abstract (EN)
Community-based Question Answering (CQA) services are becoming popular as the public gets used to look for help and obtain information. Existing CQA services try to recommend someone for answering new questions. On the other hand, people are allowed to exchange information and experience using various collaborative tools. It would be interesting to combine the two approaches to increase the reliability of recommending an answerer. Thus, relying on semantically modeled traces, we propose a comprehensive approach that recommends an answerer in a collaborative environment. From a global point of view, this approach consists in evaluating users by the performance in the CQA services and the corresponding knowledge sharing activities in which they participated in a collaborative context. By modeling and analyzing users' behavior, we assess the competency of an answerer in a particular collaborative context.

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