Authors:
David Werner
and
Christophe Cruz
Affiliation:
Université de Bourgogne, France
Keyword(s):
Recommender System, News, Domain Ontology, Ontologies, Knowledge Base, Indexing, Recommendation, Vector Space Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Ontology and the Semantic Web
;
Personalized Web Sites and Services
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
Contractors, commercial and business decision-makers need economical information to drive their
decisions. The production and distribution of a press review about French regional economic actors
represents a prospecting tool on partners and competitors for the businessman. Our goal is to propose a
customized review for each user, thus reducing the overload of useless information. Some systems for
recommending news items already exist. The usefulness of external knowledge to improve the process has
already been explained in information retrieval. The system’s knowledge base includes the domain
knowledge used during the recommendation process. Our recommender system architecture is standard, but
during the indexing task, the representations of content of each article and interests of users’ profiles created
are based on this domain knowledge. Articles and Profiles are semantically defined in the Knowledge base
via concepts, instances and relations. This paper deals with the similarity
measure, a critical subtask in
recommendation systems. The Vector Space Model is a well-known model used for relevance ranking. The
problematic exposed here is the utilization of the standard VSM method with our indexing method.
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