Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization

Technological surveillance in research centers and universities focuses on carrying out a systematic follow-up on the development of research lines, the research leaders, the possibilities of scientific-technological collaboration, and to the knowledge of current trends from research. All these elem...

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Autores:
amelec, viloria
Pineda Lezama, Omar Bonerge
Reniz, Javier
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/4839
Acceso en línea:
https://hdl.handle.net/11323/4839
https://repositorio.cuc.edu.co/
Palabra clave:
technological surveillance
collaborative filtering
recommendation system
academic context
research centers
multidimensionality
factorization tensor
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/4839
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
title Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
spellingShingle Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
technological surveillance
collaborative filtering
recommendation system
academic context
research centers
multidimensionality
factorization tensor
title_short Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
title_full Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
title_fullStr Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
title_full_unstemmed Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
title_sort Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
dc.creator.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
Reniz, Javier
dc.contributor.author.spa.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
Reniz, Javier
dc.subject.spa.fl_str_mv technological surveillance
collaborative filtering
recommendation system
academic context
research centers
multidimensionality
factorization tensor
topic technological surveillance
collaborative filtering
recommendation system
academic context
research centers
multidimensionality
factorization tensor
description Technological surveillance in research centers and universities focuses on carrying out a systematic follow-up on the development of research lines, the research leaders, the possibilities of scientific-technological collaboration, and to the knowledge of current trends from research. All these elements allow guiding the researches and supporting the scientific-technological strategy. This research proposes a model of technological surveillance supported by a recommendation system as an application that focuses on the preferences of researchers in universities and research centers. The multidimensional tensor factorization approach, based on grouping to build a recommendation system and to validate the increase in tensors, improves the accuracy of the recommendation. The experiments have been carried out in real data sets as the university and research centers. The results confirm that the dispersion issues are improved within a wider margin in both data sets. In addition, the proposed approach states that the increase in the number of dimensions shows a 7-10% improvement in accuracy and memory, which increases performance as an expert recommendation system.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-06-10T13:57:09Z
dc.date.available.none.fl_str_mv 2019-06-10T13:57:09Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 0000-2010
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/4839
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0000-2010
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/4839
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Gaitán-Angulo M. Amelec Viloria, Jenny-Paola Lis-Gutiérrez, Dionicio Neira, Enrrique López, Ernesto Joaquín Steffens Sanabria, Claudia Patricia Fernández Castro. (2018) Influence of the Management of the Innovation in the Business Performance of the Family Business: Application to the Printing Sector in Colombia. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [2] Lim, H., & Kim, H. J. (2017). Item recommendation using tag emotion in social cataloging services. Expert Systems with Applications, 89, 179-187. [3] Balasubramanian, K., Kim, J., Puretskiy, A., Berry, M. W., & Park, H. (2010). A fast algorithm for nonnegative tensor factorization using block coordinate descent and an active-set-type method. Text Mining. [4] Bobadilla, J., Hernando, A., Ortega, F., & Bernal, J. (2011). A framework for collaborative filtering recommender systems. Expert Systems with Applications, 38(12), 14609-14623. [5] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749. [6] Arora, A., Taneja, V., Parashar, S., & Mishra, A. (2016). Cross-domain based event recommendation using tensor factorization. Open Computer Science, 6(1). [7] Harper, F. M., & Konstan, J. A. (2016). The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4), 19. [8] Lee, J., Lee, D., Lee, Y. C., Hwang, W. S., & Kim, S. W. (2016). Improving the accuracy of top-n recommendation using a preference model. Information Sciences, 348, 290-304. [9] Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3), 455-500. [10] Bokde, D., Girase, S., & Mukhopadhyay, D. (2015). Matrix factorization model in collaborative filtering algorithms: A survey. Procedia Computer Science, 49, 136-146. [11] Frolov, E., & Oseledets, I. (2017). Tensor methods and recommender systems. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3). [12] Braunhofer, M., & Ricci, F. (2017). Selective contextual information acquisition in travel recommender systems. Information Technology & Tourism, 17(1), 5-29. [13] Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273. [14] Gogna, A., & Majumdar, A. (2015). Matrix completion incorporating auxiliary information for recommender system design. Expert Systems with Applications, 42(14), 5789-5799. [15] Lis-Gutiérrez JP., Gaitán-Angulo M., Lis-Gutiérrez M., Viloria A., Cubillos J., Rodríguez-Garnica PA. (2018) Electronic and Traditional Savings Accounts in Colombia: A Spatial Agglomeration Model. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [16] Baltrunas, L., & Ricci, F. (2014). Experimental evaluation of context-dependent collaborative filtering using item splitting. User Modeling and User-Adapted Interaction, 24(1-2), 7-34. [17] Kamatkar S.J., Tayade A., Viloria A., Hernández-Chacín A. (2018)a . Application of Classification Technique of Data Mining for Employee Management System. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham. [18] Kamatkar S.J., Kamble A., Viloria A., Hernández-Fernandez L., Cali E.G. (2018)b. Database Performance Tuning and Query Optimization. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham
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spelling amelec, viloriaPineda Lezama, Omar BonergeReniz, Javier2019-06-10T13:57:09Z2019-06-10T13:57:09Z20190000-2010https://hdl.handle.net/11323/4839Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Technological surveillance in research centers and universities focuses on carrying out a systematic follow-up on the development of research lines, the research leaders, the possibilities of scientific-technological collaboration, and to the knowledge of current trends from research. All these elements allow guiding the researches and supporting the scientific-technological strategy. This research proposes a model of technological surveillance supported by a recommendation system as an application that focuses on the preferences of researchers in universities and research centers. The multidimensional tensor factorization approach, based on grouping to build a recommendation system and to validate the increase in tensors, improves the accuracy of the recommendation. The experiments have been carried out in real data sets as the university and research centers. The results confirm that the dispersion issues are improved within a wider margin in both data sets. In addition, the proposed approach states that the increase in the number of dimensions shows a 7-10% improvement in accuracy and memory, which increases performance as an expert recommendation system.amelec, viloria-b470a232-0d25-444c-89a8-5f8f2c721f8b-600Pineda Lezama, Omar Bonerge-365a03a0-145e-4df5-9abe-f5ccf9d96612-0Reniz, Javier-aba8a2cf-0808-4f66-b84b-356b10eaab2e-0engProcedia Computer ScienceAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2technological surveillancecollaborative filteringrecommendation systemacademic contextresearch centersmultidimensionalityfactorization tensorRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor FactorizationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Gaitán-Angulo M. Amelec Viloria, Jenny-Paola Lis-Gutiérrez, Dionicio Neira, Enrrique López, Ernesto Joaquín Steffens Sanabria, Claudia Patricia Fernández Castro. (2018) Influence of the Management of the Innovation in the Business Performance of the Family Business: Application to the Printing Sector in Colombia. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [2] Lim, H., & Kim, H. J. (2017). Item recommendation using tag emotion in social cataloging services. Expert Systems with Applications, 89, 179-187. [3] Balasubramanian, K., Kim, J., Puretskiy, A., Berry, M. W., & Park, H. (2010). A fast algorithm for nonnegative tensor factorization using block coordinate descent and an active-set-type method. Text Mining. [4] Bobadilla, J., Hernando, A., Ortega, F., & Bernal, J. (2011). A framework for collaborative filtering recommender systems. Expert Systems with Applications, 38(12), 14609-14623. [5] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749. [6] Arora, A., Taneja, V., Parashar, S., & Mishra, A. (2016). Cross-domain based event recommendation using tensor factorization. Open Computer Science, 6(1). [7] Harper, F. M., & Konstan, J. A. (2016). The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4), 19. [8] Lee, J., Lee, D., Lee, Y. C., Hwang, W. S., & Kim, S. W. (2016). Improving the accuracy of top-n recommendation using a preference model. Information Sciences, 348, 290-304. [9] Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3), 455-500. [10] Bokde, D., Girase, S., & Mukhopadhyay, D. (2015). Matrix factorization model in collaborative filtering algorithms: A survey. Procedia Computer Science, 49, 136-146. [11] Frolov, E., & Oseledets, I. (2017). Tensor methods and recommender systems. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3). [12] Braunhofer, M., & Ricci, F. (2017). Selective contextual information acquisition in travel recommender systems. Information Technology & Tourism, 17(1), 5-29. [13] Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273. [14] Gogna, A., & Majumdar, A. (2015). Matrix completion incorporating auxiliary information for recommender system design. Expert Systems with Applications, 42(14), 5789-5799. [15] Lis-Gutiérrez JP., Gaitán-Angulo M., Lis-Gutiérrez M., Viloria A., Cubillos J., Rodríguez-Garnica PA. (2018) Electronic and Traditional Savings Accounts in Colombia: A Spatial Agglomeration Model. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [16] Baltrunas, L., & Ricci, F. (2014). Experimental evaluation of context-dependent collaborative filtering using item splitting. User Modeling and User-Adapted Interaction, 24(1-2), 7-34. [17] Kamatkar S.J., Tayade A., Viloria A., Hernández-Chacín A. (2018)a . Application of Classification Technique of Data Mining for Employee Management System. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham. [18] Kamatkar S.J., Kamble A., Viloria A., Hernández-Fernandez L., Cali E.G. (2018)b. Database Performance Tuning and Query Optimization. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, ChamPublicationORIGINALRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization.pdfRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization.pdfapplication/pdf668803https://repositorio.cuc.edu.co/bitstreams/e5d9ae7e-516d-4d56-8f91-5763207005ef/download04d1d255781a0e84932a4795cd4729b5MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/9293cb89-45f3-4d57-96c5-89a160f5adf1/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/bd1a570b-fa4b-4ad0-86f8-cd21f7374525/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization.pdf.jpgRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization.pdf.jpgimage/jpeg48664https://repositorio.cuc.edu.co/bitstreams/940f3107-ad1b-492d-ac07-18d1145ca8ab/downloadaf30aad9a878663222fdb71322c9f9a6MD55TEXTRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization.pdf.txtRecommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization.pdf.txttext/plain22779https://repositorio.cuc.edu.co/bitstreams/e1e040d7-57cc-490d-9e13-626e6617bc38/downloade27def7f58aee4864879cca2bcff99d4MD5611323/4839oai:repositorio.cuc.edu.co:11323/48392024-09-17 14:11:07.308http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

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