INVESTIGADORES
CHESÑEVAR Carlos Ivan
capítulos de libros
Título:
Recommender Systems based on Argumentation
Autor/es:
CARLOS CHESÑEVAR; ANA MAGUITMAN; GUILLERMO SIMARI,
Libro:
Emerging Artificial Intelligence Applications in Computer Engineering
Editorial:
IOS Press
Referencias:
Lugar: Amsterdam, Holanda; Año: 2007; p. 53 - 70
Resumen:
In recent years there has been a wide-spread evolution of support tools
that help users to accomplish a range of computer-mediated tasks. In this context,
recommender systems have emerged as powerful user-support tools which provide
assistance to users by facilitating access to relevant items. Nevertheless, recommender
system technologies suffer from a number of limitations, mainly due to
the lack of underlying elements for performing qualitative reasoning appropriately.
Over the last few years, argumentation has been gaining increasing importance in
several AI-related areas, mainly as a vehicle for facilitating rationally justifiable decision
making when handling incomplete and potentially inconsistent information.
In this setting, recommender systems can rely on argumentation techniques by providing
reasoned guidelines or hints supported by a rationally justified procedure.
This chapter presents a generic argument-based approach to characterize recommender
system technologies, in which knowledge representation and inference are
captured in terms of Defeasible Logic Programming, a general-purpose defeasible
argumentation formalism based on logic programming. As a particular instance of
our approach we analyze an argument-based search engine called ARGUENET, an
application oriented towards providing recommendations on the web scenario.
application oriented towards providing recommendations on the web scenario.
RGUENET, an
application oriented towards providing recommendations on the web scenario.