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Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization
dc.contributor.author | amelec, viloria | spa |
dc.contributor.author | Pineda Lezama, Omar Bonerge | spa |
dc.contributor.author | Reniz, Javier | spa |
dc.date.accessioned | 2019-06-10T13:57:09Z | |
dc.date.available | 2019-06-10T13:57:09Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 0000-2010 | spa |
dc.identifier.uri | http://hdl.handle.net/11323/4839 | spa |
dc.description.abstract | 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. | spa |
dc.language.iso | eng | |
dc.publisher | Procedia Computer Science | spa |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
dc.subject | technological surveillance | spa |
dc.subject | collaborative filtering | spa |
dc.subject | recommendation system | spa |
dc.subject | academic context | spa |
dc.subject | research centers | spa |
dc.subject | multidimensionality | spa |
dc.subject | factorization tensor | spa |
dc.title | Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization | spa |
dc.type | Artículo de revista | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
dc.relation.references | [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 | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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