ISISTAN   23985
INSTITUTO SUPERIOR DE INGENIERIA DEL SOFTWARE
Unidad Ejecutora - UE
artículos
Título:
Folksonomy-based Recommender Systems - A State-of-the-Art Review (Indexed SCI, IF JCR2014=1.886)
Autor/es:
DANIELA GODOY; ALEJANDRO CORBELLINI
Revista:
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Editorial:
JOHN WILEY & SONS INC
Referencias:
Lugar: New York; Año: 2015
ISSN:
0884-8173
Resumen:
Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g. Web pages, pictures and video) using open-ended tags. The rapid adoption of these systems has lead to an increasing amount of users providing information about themselves and, at the same time, a growing corpus of rich social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied with recommendation purposes. This review aims at providing a comprehensive overview of the literature in the eld of folksonomy-based recommender systems. Current recommendation approaches stemming from elds such as user modeling, collaborative ltering, content and link-analysis are reviewed and discussed in order to provide a starting point for researchers in the eld as well as identify future research lines.