Autor según el artículo: Guimera, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta
Departamento: Enginyeria Química
Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta
Palabras clave: Obesity Blockmodels
Resumen: With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.
Áreas temáticas: Zootecnia / recursos pesqueiros Sociology Sociología Serviço social Saúde coletiva Química Psychology Psicología Planejamento urbano e regional / demografia Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Letras / linguística Interdisciplinary research in the social sciences Interdisciplinar Human geography and urban studies History & philosophy of science Historia Geografía Geociências General medicine General biochemistry,genetics and molecular biology General agricultural and biological sciences Farmacia Environmental studies Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação física Educação Economia Direito Demography Comunicação e informação Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência política e relações internacionais Ciência de alimentos Ciência da computação Biotecnología Biology Biodiversidade Biochemistry, genetics and molecular biology (miscellaneous) Astronomia / física Arquitetura, urbanismo e design Archaeology Antropologia / arqueologia Anthropology Agricultural and biological sciences (miscellaneous) Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat
Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525
Fecha de alta del registro: 2024-10-19
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0044620
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Plos One. 7 (9): e44620-
Referencia de l'ítem segons les normes APA: Guimera, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta (2012). Predicting Human Preferences Using the Block Structure of Complex Social Networks. Plos One, 7(9), e44620-. DOI: 10.1371/journal.pone.0044620
DOI del artículo: 10.1371/journal.pone.0044620
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2012
Tipo de publicación: Journal Publications
Repositori URV