Authors:
Marie Françoise Canut
1
;
Manel Mezghani
2
;
Sirinya On-At
1
;
André Péninou
1
and
Florence Sèdes
1
Affiliations:
1
University of Toulouse, France
;
2
University of Toulouse and Sfax University, France
Keyword(s):
User’s Profile, User’s Interests, Tags, Social Network Analysis, Egocentric Network, Community.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Society, e-Business and e-Government
;
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:
With the growing amount of social media contents, the user needs more accurate information that reflects
his interests. We focus on deriving user’s profile and especially user’s interests, which are key elements
to improve adaptive mechanisms in information systems (e.g. recommendation, customization). In this
paper, we are interested in studying two approaches of user’s profile derivation from egocentric networks:
individual-based approach and community-based approach. As these approaches have been previously applied
in a co-author network and have shown their efficiency, we are interested in comparing them in the context
of social annotations or tags. The motivation to use tagging information is that tags are proved relevant
by many researches to describe user’s interests. The evaluation in Delicious social databases shows that
the individual-based approach performs well when the semantic weight of user’s interests is taken more in
consideration and the community-based approach per
forms better in the opposite case. We also take into
consideration the dynamic of social tagging networks. To study the influence of time in the efficiency of the
two user’s profile derivation approaches, we have applied a time-awareness method in our comparative study.
The evaluation in Delicious demonstrates the importance of taking into account the dynamic of social tagging
networks to improve effectiveness of the tag-based user profiling approaches.
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