Autor:
Guerra, Francesco

Foto de perfil

E-mails conocidos

francesco.guerra@unimore.it

Fecha de nacimiento

Proyectos de investigación

Unidades organizativas

Puesto de trabajo

Apellidos

Guerra

Nombre de pila

Francesco

Nombre

Nombres alternativos

Afiliaciones conocidas

University of Modena and Reggio Emilia, Modena, Italy

Páginas web conocidas

Página completa del ítem
Notificar un error en este autor

Filtros

20151
Restablecer filtros

Ajustes

Autor: Guerra, Francesco × Tema: Recommendation Systems ×

Resultados de la búsqueda

Mostrando 1 - 1 de 1
  • Miniatura
    Artículo
    A First Step Towards Keyword-Based Searching for Recommendation Systems
    Rodríguez Hernández, María Del Carmen; Guerra, Francesco; Ilarri, Sergio; Trillo-Lado, Raquel. Actas de las XX Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2015), 2015-09-15.
    Due to the high availability of data, users are frequently overloaded with a huge amount of alternatives when they need to choose a particular item. This has motivated an increased interest in research on recommendation systems, which filter the options and provide users with suggestions about specific elements (e.g., movies, restaurants, hotels, news, etc.) that are estimated to be potentially relevant for the user. Recommendation systems are still an active area of research, and particularly in the last years the concept of context-aware recommendation systems has started to be popular, due to the interest of considering the context of the user in the recommendation process. In this paper, we describe our work-in-progress concerning pull-based recommendations (i.e., recommendations about certain types of items that are explicitly requested by the user). In particular, we focus on the problem of detecting the type of item the user is interested in. Due to its popularity, we consider a keyword-based user interface: the user types a few keywords and the system must determine what the user is searching for. Whereas there is extensive work in the field of keyword-based search, which is still a very active research area, keyword searching has not been applied so far in most recommendation contexts.