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

• Published in: Data & knowledge engineering Vol. 108; pp. 15 - 29
• Main Authors: Mezghani, Manel, Péninou, André, Zayani, Corinne Amel, Amous, Ikram, Sèdes, Florence
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2017; Elsevier
• Subjects: Adaptation; Computer Science; Indexation; Indexing methods; Information Retrieval; Information Theory; Resource; Semi-structured data and XML; Social network; Tagging behaviour; User interests; Tagging behaviour; User interests; Indexation; Resource; Social network; Indexing methods; Adaptation; Semi-structured data and XML
• ISSN: 0169-023X; 1872-6933
• DOI: 10.1016/j.datak.2016.12.003

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.