Articles producció científica> Enginyeria Química

Accurate and scalable social recommendation using mixed-membership stochastic block models

  • Datos identificativos

    Identificador: imarina:9298196
    Handle: http://hdl.handle.net/20.500.11797/imarina9298196
  • Autores:

    Godoy-Lorite, Antonia
    Guimera, Roger
    Moore, Cristopher
    Sales-Pardo, Marta
  • Otros:

    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: 2023-05-20
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.pnas.org/doi/full/10.1073/pnas.1606316113
    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
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1073/pnas.1606316113
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2016
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Multidisciplinary,Multidisciplinary Sciences
    Stochastic block model
    Social recommendation
    Scalable algorithm
    Recommender systems
    Collaborative filtering
    social recommendation
    scalable algorithm
    recommender systems
    collaborative filtering
    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
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