Modeling the evolution of item rating networks using time-domain preferential attachment

Lavia, Edmundo F.; Chernomoretz, Ariel; Martín Buldú, Javier; Zanin, Massimiliano y Balenzuela, Pablo (2012). Modeling the evolution of item rating networks using time-domain preferential attachment. "International Journal of Bifurcation and Chaos", v. 22 (n. 7); pp. 1250180-1250192. ISSN 0218-1274. https://doi.org/10.1142/S0218127412501805.

Descripción

Título: Modeling the evolution of item rating networks using time-domain preferential attachment
Autor/es:
  • Lavia, Edmundo F.
  • Chernomoretz, Ariel
  • Martín Buldú, Javier
  • Zanin, Massimiliano
  • Balenzuela, Pablo
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Bifurcation and Chaos
Fecha: Julio 2012
Volumen: 22
Materias:
Escuela: Centro de Tecnología Biomédica (CTB) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The understanding of the structure and dynamics of the intricate network of connections among people that consumes products through Internet appears as an extremely useful asset in order to study emergent properties related to social behavior. This knowledge could be useful, for example, to improve the performance of personal recommendation algorithms. In this contribution, we analyzed five-year records of movie-rating transactions provided by Netflix, a movie rental platform where users rate movies from an online catalog. This dataset can be studied as a bipartite user-item network whose structure evolves in time. Even though several topological properties from subsets of this bipartite network have been reported with a model that combines random and preferential attachment mechanisms [Beguerisse Díaz et al., 2010], there are still many aspects worth to be explored, as they are connected to relevant phenomena underlying the evolution of the network. In this work, we test the hypothesis that bursty human behavior is essential in order to describe how a bipartite user-item network evolves in time. To that end, we propose a novel model that combines, for user nodes, a network growth prescription based on a preferential attachment mechanism acting not only in the topological domain (i.e. based on node degrees) but also in time domain. In the case of items, the model mixes degree preferential attachment and random selection. With these ingredients, the model is not only able to reproduce the asymptotic degree distribution, but also shows an excellent agreement with the Netflix data in several time-dependent topological properties.

Más información

ID de Registro: 20185
Identificador DC: http://oa.upm.es/20185/
Identificador OAI: oai:oa.upm.es:20185
Identificador DOI: 10.1142/S0218127412501805
URL Oficial: http://www.worldscientific.com/doi/abs/10.1142/S0218127412501805
Depositado por: Memoria Investigacion
Depositado el: 11 Oct 2013 18:07
Ultima Modificación: 21 Abr 2016 22:41
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