Authors:
Aroua Essayeh
and
Mourad Abed
Affiliation:
LAMIH, France
Keyword(s):
Clustering, Learning Machine, Ontology, Public Transportation.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Engineering
;
Enterprise Information Systems
;
Group Decision Support Systems
;
Information Systems Analysis and Specification
;
Intelligent Transportation System
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
In traditional transport information systems, the users must explicitly provide the information related to both their profiles and travels to receive a personalized response. However, this requires, among others, an extra effort from user in term of search time. We aim to identify not only implicitly users’ information, but also to anticipate their need even if some data are missing through a recommender system based on collaborative filtering technique. In this work, the information related to users is represented using the ontology which proved far more adequate model for representing semantically data.