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dc.creatorSilva, Jesús
dc.creatorVarela, Noel
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorHernández-P, Hugo
dc.creatorMartínez Ventura, Jairo
dc.creatorde la Hoz, Boris
dc.creatorPérez Coronel, Leidy
dc.date.accessioned2019-08-08T14:06:12Z
dc.date.available2019-08-08T14:06:12Z
dc.date.issued2019-06-26
dc.identifier.isbn978-3-030-22808-8
dc.identifier.isbn978-3-030-22807-1
dc.identifier.urihttp://hdl.handle.net/11323/5131
dc.description.abstractThe choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy.es_ES
dc.language.isoenges_ES
dc.publisherInternational Symposium on Neural Networkses_ES
dc.relation.ispartofhttps://doi.org/10.1007/978-3-030-22808-8_21es_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectCollaborative filteringes_ES
dc.subjectContext aware recommendation systemes_ES
dc.subjectContextual Modelinges_ES
dc.subjectItem recommendationses_ES
dc.subjectMulti-dimensionalityes_ES
dc.subjectTensor Factorizationes_ES
dc.titleMulti-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profileses_ES
dc.typePreprintes_ES
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dc.type.hasVersioninfo:eu-repo/semantics/draftes_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


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