Authors:
Manel Mezghani
1
;
André Péninou
2
;
Corinne Amel Zayani
3
;
Ikram Amous
3
and
Florence Sèdes
2
Affiliations:
1
Sfax University and Paul Sabatier University, Tunisia
;
2
Paul Sabatier University, France
;
3
Sfax University, Tunisia
Keyword(s):
User Interests, Tagging Behaviour, Resources, Social Network, Adaptation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative and Social Interaction
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web 2.0 and Social Networking Controls
;
Web Information Systems and Technologies
Abstract:
The social user is characterized by his social activity like sharing information, making relationships, etc. With the evolution of social content, the user needs more accurate information that reflects his interests. We focus on analyzing user's interests which are key elements for improving adaptation (recommendation, personalization, etc.). In this article, we are interested to overcome issues that influence the quality of adaptation in social networks, such as the accuracy of user's interests. The originality of our approach is the proposal of a new technique of user's interests detection by analyzing the accuracy of the tagging behaviour of the users in order to figure out the tags which really reflect the resources content. We focus on semi-structured data (resources), since they provide more comprehensible information. Our approach has been tested and evaluated in Delicious social database. A comparison between our approach and classical tag-based approach shows that our approa
ch performs better.
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