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

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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...

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Autores principales: Lavia, E.F., Chernomoretz, A., Buldú, J.M., Zanin, M., Balenzuela, P.
Formato: JOUR
Materias:
bipartite networks
bursting
Complex networks
Netflix
recommendation systems
Behavioral research
Motion pictures
Recommender systems
Bipartite network
Degree distributions
Personal recommendations
Preferential attachments
Structure and dynamics
Topological properties
Topology
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_02181274_v22_n7_p_Lavia
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_02181274_v22_n7_p_Lavia
record_format dspace
spelling todo:paper_02181274_v22_n7_p_Lavia2023-10-03T15:10:45Z Modeling the evolution of item rating networks using time-domain preferential attachment Lavia, E.F. Chernomoretz, A. Buldú, J.M. Zanin, M. Balenzuela, P. bipartite networks bursting Complex networks Netflix recommendation systems Behavioral research Complex networks Motion pictures Recommender systems Bipartite network bursting Degree distributions Netflix Personal recommendations Preferential attachments Structure and dynamics Topological properties Topology 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. © 2012 World Scientific Publishing Company. Fil:Chernomoretz, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Balenzuela, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_02181274_v22_n7_p_Lavia
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic bipartite networks
bursting
Complex networks
Netflix
recommendation systems
Behavioral research
Complex networks
Motion pictures
Recommender systems
Bipartite network
bursting
Degree distributions
Netflix
Personal recommendations
Preferential attachments
Structure and dynamics
Topological properties
Topology
spellingShingle bipartite networks
bursting
Complex networks
Netflix
recommendation systems
Behavioral research
Complex networks
Motion pictures
Recommender systems
Bipartite network
bursting
Degree distributions
Netflix
Personal recommendations
Preferential attachments
Structure and dynamics
Topological properties
Topology
Lavia, E.F.
Chernomoretz, A.
Buldú, J.M.
Zanin, M.
Balenzuela, P.
Modeling the evolution of item rating networks using time-domain preferential attachment
topic_facet bipartite networks
bursting
Complex networks
Netflix
recommendation systems
Behavioral research
Complex networks
Motion pictures
Recommender systems
Bipartite network
bursting
Degree distributions
Netflix
Personal recommendations
Preferential attachments
Structure and dynamics
Topological properties
Topology
description 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. © 2012 World Scientific Publishing Company.
format JOUR
author Lavia, E.F.
Chernomoretz, A.
Buldú, J.M.
Zanin, M.
Balenzuela, P.
author_facet Lavia, E.F.
Chernomoretz, A.
Buldú, J.M.
Zanin, M.
Balenzuela, P.
author_sort Lavia, E.F.
title Modeling the evolution of item rating networks using time-domain preferential attachment
title_short Modeling the evolution of item rating networks using time-domain preferential attachment
title_full Modeling the evolution of item rating networks using time-domain preferential attachment
title_fullStr Modeling the evolution of item rating networks using time-domain preferential attachment
title_full_unstemmed Modeling the evolution of item rating networks using time-domain preferential attachment
title_sort modeling the evolution of item rating networks using time-domain preferential attachment
url http://hdl.handle.net/20.500.12110/paper_02181274_v22_n7_p_Lavia
work_keys_str_mv AT laviaef modelingtheevolutionofitemratingnetworksusingtimedomainpreferentialattachment
AT chernomoretza modelingtheevolutionofitemratingnetworksusingtimedomainpreferentialattachment
AT buldujm modelingtheevolutionofitemratingnetworksusingtimedomainpreferentialattachment
AT zaninm modelingtheevolutionofitemratingnetworksusingtimedomainpreferentialattachment
AT balenzuelap modelingtheevolutionofitemratingnetworksusingtimedomainpreferentialattachment
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