Autor según el artículo: Godoy-Lorite, Antonia; Guimera, Roger; Moore, Cristopher; Sales-Pardo, Marta;
Departamento: Enginyeria Química
Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta
Palabras clave: Stochastic block model Social recommendation Scalable algorithm Recommender systems Collaborative filtering social recommendation scalable algorithm recommender systems collaborative filtering
Resumen: With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.
Áreas temáticas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Matemática / probabilidade e estatística Interdisciplinar Geografía Geociências General o multidisciplinar Farmacia Engenharias iv Engenharias iii Engenharias ii Engenharias i Educação física Ciencias sociales Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Antropologia / arqueologia Anthropology
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: 2024-07-20
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.pnas.org/doi/full/10.1073/pnas.1606316113
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Proceedings Of The National Academy Of Sciences Of The United States Of America. 113 (50): 14207-14212
Referencia de l'ítem segons les normes APA: Godoy-Lorite, Antonia; Guimera, Roger; Moore, Cristopher; Sales-Pardo, Marta; (2016). Accurate and scalable social recommendation using mixed-membership stochastic block models. Proceedings Of The National Academy Of Sciences Of The United States Of America, 113(50), 14207-14212. DOI: 10.1073/pnas.1606316113
DOI del artículo: 10.1073/pnas.1606316113
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2016
Tipo de publicación: Journal Publications
Repositori URV