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A Semi-Supervised Algorithm to Manage Communities of Interests

A Semi-Supervised Algorithm to Manage Communities of Interests

Pascal Francq
ISBN13: 9781615208418|ISBN10: 1615208410|ISBN13 Softcover: 9781616923617|EISBN13: 9781615208425
DOI: 10.4018/978-1-61520-841-8.ch006
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MLA

Francq, Pascal. "A Semi-Supervised Algorithm to Manage Communities of Interests." Collaborative Search and Communities of Interest: Trends in Knowledge Sharing and Assessment, edited by Pascal Francq, IGI Global, 2011, pp. 98-133. https://doi.org/10.4018/978-1-61520-841-8.ch006

APA

Francq, P. (2011). A Semi-Supervised Algorithm to Manage Communities of Interests. In P. Francq (Ed.), Collaborative Search and Communities of Interest: Trends in Knowledge Sharing and Assessment (pp. 98-133). IGI Global. https://doi.org/10.4018/978-1-61520-841-8.ch006

Chicago

Francq, Pascal. "A Semi-Supervised Algorithm to Manage Communities of Interests." In Collaborative Search and Communities of Interest: Trends in Knowledge Sharing and Assessment, edited by Pascal Francq, 98-133. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-841-8.ch006

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Abstract

This chapter presents a genetic algorithm, called the Similarity-based Clustering Genetic Algorithm (SCGA), used to group users‘ profiles. This algorithm is integrated in an approach which allows to share documents among users browsing a collection of documents. The users are described in terms of profiles, with each profile corresponding to one area of interest. While browsing through the collection of documents, users‘ profiles are computed. These profiles are then grouped into communities of interests using the SCGA which is based on the Grouping Genetic Algorithm (GGA). In fact, the SCGA can solve other similar problems under certain circumstances. The approach is part of a more generic model to manage information called the GALILEI Framework. This framework, which provides promising results, has been developed in a software platform available under the GNU GPL license.

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