6533b859fe1ef96bd12b8886
RESEARCH PRODUCT
PROFILE REFINEMENT IN ONTOLOGY-BASED RECOMMANDER SYSTEMS FOR ECONOMICAL E-NEWS
Hassan ThomasDavid WernerAurélie BertauxChristophe Cruzsubject
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]recommender system[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]profile refinement[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Semantic web.[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]e-newsontology[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]description
International audience; This paper is interested in a recommender system of economic news articles. More precisely, it focuses on automatic profile refinement of customers which is an important task over time by taken into account logs of the user concerning especially his/her actions, reading time, and domain specific knowledge. In our approach, ontologies are used to describe and automatically refine these profiles. This work focuses on one particular type of recommender systems which is content-based recommenders. The aim of these recommender systems is to build a user profile and to improve its precision over time. Several improvements that have been made to these recommender systems over the last decade are analyzed. We find that the improvements brought by the use of semantic knowledge are not negligible, therefore semantic web approaches should be more and more used in the future. Nevertheless improvements remain possible in this domain and further research could be interesting.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-05-15 |