Author, as appears in the article.: Guimera, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta
Department: Enginyeria Química
URV's Author/s: Guimera Manrique, Roger / Sales Pardo, Marta
Keywords: Obesity Blockmodels
Abstract: 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: roger.guimera@urv.cat marta.sales@urv.cat
Author identifier: 0000-0002-3597-4310 0000-0002-8140-6525
Record's date: 2024-10-19
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0044620
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Plos One. 7 (9): e44620-
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
Article's DOI: 10.1371/journal.pone.0044620
Entity: Universitat Rovira i Virgili
Journal publication year: 2012
Publication Type: Journal Publications
Repositori URV