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Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information

Mezghani, Manel and Péninou, André and Zayani, Corinne Amel and Amous, Ikram and Sèdes, Florence Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information. (2017) Data and Knowledge Engineering, 108. 15-29. ISSN 0169-023X

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Official URL: http://dx.doi.org/10.1016/j.datak.2016.12.003

Abstract

Social media provides an environment of information exchange. They principally rely on their users to create content, to annotate others’ content and to make on-line relationships. The user activities reflect his opinions, interests, etc. in this environment. We focus on analysing this social environment to detect user interests which are the key elements for improving adaptation. This choice is motivated by the lack of information in the user profile and the inefficiency of the information issued from methods that analyse the classic user behaviour (e.g. navigation, time spent on web page, etc.). So, having to cope with an incomplete user profile, the user social network can be an important data source to detect user interests. The originality of our approach is based on the proposal of a new technique of interests' detection by analysing the accuracy of the tagging behaviour of a user in order to figure out the tags which really reflect the content of the resources. So, these tags are somehow comprehensible and can avoid tags “ambiguity” usually associated to these social annotations. The approach combines the tag, user and resource in a way that guarantees a relevant interests detection. The proposed approach has been tested and evaluated in the Delicious social database. For the evaluation, we compare the result issued from our approach using the tagging behaviour of the neighbours (the egocentric network and the communities) with the information yet known for the user (his profile). A comparative evaluation with the classical tag-based method of interests detection shows that the proposed approach is better.

Item Type:Article
Additional Information:Thanks to Elsevier editor. This papers appears in volume 108 of Data and Knowledge Engineering ISSN: 0169-023X The definitive version is available at: http://www.sciencedirect.com The original PDF of the article can be found at : https://www.sciencedirect.com/science/article/pii/S0169023X16303718
HAL Id:hal-01709183
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Sfax (TUNISIA)
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Deposited By: IRIT IRIT
Deposited On:26 Jan 2018 14:53

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