Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems

Detalhes bibliográficos
Autor(a) principal: Domingues,MA
Data de Publicação: 2013
Outros Autores: Alípio Jorge, Carlos Manuel Soares
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/4559
http://dx.doi.org/10.1016/j.ipm.2012.07.009
Resumo: Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user-item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.
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spelling Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systemsTraditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user-item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.2017-12-20T18:33:13Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4559http://dx.doi.org/10.1016/j.ipm.2012.07.009engDomingues,MAAlípio JorgeCarlos Manuel Soaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:47Zoai:repositorio.inesctec.pt:123456789/4559Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:37.657456Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
title Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
spellingShingle Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Domingues,MA
title_short Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
title_full Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
title_fullStr Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
title_full_unstemmed Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
title_sort Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
author Domingues,MA
author_facet Domingues,MA
Alípio Jorge
Carlos Manuel Soares
author_role author
author2 Alípio Jorge
Carlos Manuel Soares
author2_role author
author
dc.contributor.author.fl_str_mv Domingues,MA
Alípio Jorge
Carlos Manuel Soares
description Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user-item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
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http://dx.doi.org/10.1016/j.ipm.2012.07.009
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