Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem

Recommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborative filtering. This technique makes recommendations...

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Detalles Bibliográficos
Autores: Cordobés de la Calle, Héctor, Chiroque, Luis F., Fernández Anta, Antonio, García, Rafael, Morere, Philippe, Ornella, Lorenzo, Pérez, Fernando, Santos, Agustín
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:IMDEA Networks Institute
Repositorio:IMDEA Networks Institute Digital Repository
Idioma:inglés
OAI Identifier:oai:dspace.networks.imdea.org:20.500.12761/8
Acceso en línea:http://hdl.handle.net/20.500.12761/8
https://dx.doi.org/10.9781/ijimai.2015.324
Access Level:acceso abierto
Palabra clave:Recommendation engines
smartphone apps
graph theory
collaborative filtering
flow algorithms
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spelling Empirical Comparison of Graph-based Recommendation Engines for an Apps EcosystemCordobés de la Calle, HéctorChiroque, Luis F.Fernández Anta, AntonioGarcía, RafaelMorere, PhilippeOrnella, LorenzoPérez, FernandoSantos, AgustínRecommendation enginessmartphone appsgraph theorycollaborative filteringflow algorithmsRecommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborative filtering. This technique makes recommendations based on the past behavior of other users and the similarity between users and items. In this paper we have evaluated the performance of several RE based on the properties of the networks formed by users and items. The RE use in a novel way graph theoretic concepts like edges weights or network flow. The evaluation has been conducted in a real environment (ecosystem) for recommending apps to smartphone users. The analysis of the results allows concluding that the effectiveness of a RE can be improved if the age of the data, and if a global view of the data is considered. It also shows that graph-based RE are effective, but more experiments are required for a more accurate characterization of their properties.pubImaI-Software20152015-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12761/8https://dx.doi.org/10.9781/ijimai.2015.324reponame:IMDEA Networks Institute Digital Repositoryinstname:IMDEA Networks InstituteInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dspace.networks.imdea.org:20.500.12761/82026-06-06T12:35:51Z
dc.title.none.fl_str_mv Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
title Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
spellingShingle Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
Cordobés de la Calle, Héctor
Recommendation engines
smartphone apps
graph theory
collaborative filtering
flow algorithms
title_short Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
title_full Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
title_fullStr Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
title_full_unstemmed Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
title_sort Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
dc.creator.none.fl_str_mv Cordobés de la Calle, Héctor
Chiroque, Luis F.
Fernández Anta, Antonio
García, Rafael
Morere, Philippe
Ornella, Lorenzo
Pérez, Fernando
Santos, Agustín
author Cordobés de la Calle, Héctor
author_facet Cordobés de la Calle, Héctor
Chiroque, Luis F.
Fernández Anta, Antonio
García, Rafael
Morere, Philippe
Ornella, Lorenzo
Pérez, Fernando
Santos, Agustín
author_role author
author2 Chiroque, Luis F.
Fernández Anta, Antonio
García, Rafael
Morere, Philippe
Ornella, Lorenzo
Pérez, Fernando
Santos, Agustín
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Recommendation engines
smartphone apps
graph theory
collaborative filtering
flow algorithms
topic Recommendation engines
smartphone apps
graph theory
collaborative filtering
flow algorithms
description Recommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborative filtering. This technique makes recommendations based on the past behavior of other users and the similarity between users and items. In this paper we have evaluated the performance of several RE based on the properties of the networks formed by users and items. The RE use in a novel way graph theoretic concepts like edges weights or network flow. The evaluation has been conducted in a real environment (ecosystem) for recommending apps to smartphone users. The analysis of the results allows concluding that the effectiveness of a RE can be improved if the age of the data, and if a global view of the data is considered. It also shows that graph-based RE are effective, but more experiments are required for a more accurate characterization of their properties.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12761/8
https://dx.doi.org/10.9781/ijimai.2015.324
url http://hdl.handle.net/20.500.12761/8
https://dx.doi.org/10.9781/ijimai.2015.324
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ImaI-Software
publisher.none.fl_str_mv ImaI-Software
dc.source.none.fl_str_mv reponame:IMDEA Networks Institute Digital Repository
instname:IMDEA Networks Institute
instname_str IMDEA Networks Institute
reponame_str IMDEA Networks Institute Digital Repository
collection IMDEA Networks Institute Digital Repository
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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