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

Lavia, Edmundo F. and Chernomoretz, Ariel and Martín Buldú, Javier and Zanin, Massimiliano and 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.

Description

Title: Modeling the evolution of item rating networks using time-domain preferential attachment
Author/s:
  • Lavia, Edmundo F.
  • Chernomoretz, Ariel
  • Martín Buldú, Javier
  • Zanin, Massimiliano
  • Balenzuela, Pablo
Item Type: Article
Título de Revista/Publicación: International Journal of Bifurcation and Chaos
Date: July 2012
ISSN: 0218-1274
Volume: 22
Subjects:
Faculty: Centro de Tecnología Biomédica (CTB) (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 20185
DC Identifier: http://oa.upm.es/20185/
OAI Identifier: oai:oa.upm.es:20185
DOI: 10.1142/S0218127412501805
Official URL: http://www.worldscientific.com/doi/abs/10.1142/S0218127412501805
Deposited by: Memoria Investigacion
Deposited on: 11 Oct 2013 18:07
Last Modified: 21 Apr 2016 22:41
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