Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

The 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 stude...

Full description

Autores:
Silva, Jesús
Varela, Noel
Pineda Lezama, Omar Bonerge
Hernández-P, Hugo
Martínez Ventura, Jairo
de la Hoz, Boris
Pérez Coronel, Leidy
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5131
Acceso en línea:
https://hdl.handle.net/11323/5131
https://repositorio.cuc.edu.co/
Palabra clave:
Collaborative filtering
Context aware recommendation system
Contextual Modeling
Item recommendations
Multi-dimensionality
Tensor Factorization
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_51467fcc3b9fa6f0f99b5c4557a0a3a0
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5131
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
title Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
spellingShingle Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
Collaborative filtering
Context aware recommendation system
Contextual Modeling
Item recommendations
Multi-dimensionality
Tensor Factorization
title_short Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
title_full Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
title_fullStr Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
title_full_unstemmed Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
title_sort Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles
dc.creator.fl_str_mv Silva, Jesús
Varela, Noel
Pineda Lezama, Omar Bonerge
Hernández-P, Hugo
Martínez Ventura, Jairo
de la Hoz, Boris
Pérez Coronel, Leidy
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Varela, Noel
Pineda Lezama, Omar Bonerge
Hernández-P, Hugo
Martínez Ventura, Jairo
de la Hoz, Boris
Pérez Coronel, Leidy
dc.subject.spa.fl_str_mv Collaborative filtering
Context aware recommendation system
Contextual Modeling
Item recommendations
Multi-dimensionality
Tensor Factorization
topic Collaborative filtering
Context aware recommendation system
Contextual Modeling
Item recommendations
Multi-dimensionality
Tensor Factorization
description The 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.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-08-08T14:06:12Z
dc.date.available.none.fl_str_mv 2019-08-08T14:06:12Z
dc.date.issued.none.fl_str_mv 2019-06-26
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
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dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/preprint
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.isbn.spa.fl_str_mv 978-3-030-22808-8
978-3-030-22807-1
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/5131
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 978-3-030-22808-8
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dc.language.iso.none.fl_str_mv eng
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dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.1007/978-3-030-22808-8_21
dc.relation.references.spa.fl_str_mv 1. Vásquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017) Google Scholar 2. Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimento 6(1), 1–17 (2009) Google Scholar 3. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_24 Google Scholar 4. Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching – learning process through knowledge data discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_63 Google Scholar 5. Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comun. Estadística 9(1), 93–106 (2016) Google Scholar 6. Reihanian, A., Minaei-Bidgoli, B., Alizadeh, H.: Topic-oriented community detection of rating-based social networks. J. King Saud Univ. Comput. Inf. Sci. 28(3), 303–310 (2016) Google Scholar 7. Rashid, A.M.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 127–134. ACM (2002) Google Scholar 8. Gómez, S., Zervas, P., Sampson, D.G., Fabregat, R.: Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. J. King Saud Univ. Comput. Inf. Sci. 26(1), 47–61 (2014) Google Scholar 9. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_3 Google Scholar 10. Malik, F., Baharudin, B.: Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. J. King Saud Univ. Comput. Inf. Sci. 25(2), 207–218 (2013) Google Scholar 11. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 4(1), 1–12 (2009) Google Scholar 12. De Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010) Google Scholar 13. Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015) Google Scholar 14. Buder, J., Schwind, C.: Learning with personalized recommender systems: a psychological view. Comput. Hum. Behav. 28(1), 207–216 (2012) Google Scholar 15. Taneja, A., Arora, A.: Cross domain recommendation using multidimensional tensor factorization. Expert Syst. Appl. 92(1), 304–316 (2018) Google Scholar 16. Frolov, E., Oseledets, I.: Tensor methods and recommender systems. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 7(3), 1–12 (2017) Google Scholar 17. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009) MathSciNetzbMATHGoogle Scholar 18. Panniello, U., Tuzhilin, A., Gorgoglione, M.: Comparing context-aware recommender systems in terms of accuracy and diversity. User Model. User-Adap. Inter. 24(2), 35–65 (2014) Google Scholar 19. Rafailidis, D., Kefalas, P., Manolopoulos, Y.: Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst. Appl. 74(1), 11–18 (2017) Google Scholar 20. Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010) Google Scholar 21. Zheng, C., Haihong, E., Song, M., Song, J.: CMPTF: contextual modeling probabilistic tensor factorization for recommender systems. Neurocomputing 205(1), 141–151 (2016) Google Scholar 22. Hidasi, B., Tikk, D.: Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 67–82. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_5 Google Scholar 23. Lee, J., Lee, D., Lee, Y.C., Hwang, W.S., Kim, S.W.: Improving the accuracy of top-n recommendation using a preference model. Inf. Sci. 348(1), 290–304 (2016) Google Scholar 24. Vásquez, C., et al.: Cluster of the latin american universities top100 according to webometrics 2017. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_26 Google Scholar 25. Torres-Samuel, M., Vásquez, C.L., Viloria, A., Varela, N., Hernández-Fernandez, L., Portillo-Medina, R.: Analysis of patterns in the university world rankings webometrics, Shanghai, QS and SIR-SCimago: case Latin America. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 188–199. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_18
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spelling Silva, JesúsVarela, NoelPineda Lezama, Omar BonergeHernández-P, HugoMartínez Ventura, Jairode la Hoz, BorisPérez Coronel, Leidy2019-08-08T14:06:12Z2019-08-08T14:06:12Z2019-06-26978-3-030-22808-8978-3-030-22807-1https://hdl.handle.net/11323/5131Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.Silva, JesúsVarela, NoelPineda Lezama, Omar BonergeHernández-P, HugoMartínez Ventura, Jairode la Hoz, BorisPérez Coronel, LeidyengInternational Symposium on Neural Networkshttps://doi.org/10.1007/978-3-030-22808-8_211. Vásquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017) Google Scholar 2. Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimento 6(1), 1–17 (2009) Google Scholar 3. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_24 Google Scholar 4. Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching – learning process through knowledge data discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_63 Google Scholar 5. Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comun. Estadística 9(1), 93–106 (2016) Google Scholar 6. Reihanian, A., Minaei-Bidgoli, B., Alizadeh, H.: Topic-oriented community detection of rating-based social networks. J. King Saud Univ. Comput. Inf. Sci. 28(3), 303–310 (2016) Google Scholar 7. Rashid, A.M.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 127–134. ACM (2002) Google Scholar 8. Gómez, S., Zervas, P., Sampson, D.G., Fabregat, R.: Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. J. King Saud Univ. Comput. Inf. Sci. 26(1), 47–61 (2014) Google Scholar 9. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_3 Google Scholar 10. Malik, F., Baharudin, B.: Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. J. King Saud Univ. Comput. Inf. Sci. 25(2), 207–218 (2013) Google Scholar 11. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 4(1), 1–12 (2009) Google Scholar 12. De Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010) Google Scholar 13. Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015) Google Scholar 14. Buder, J., Schwind, C.: Learning with personalized recommender systems: a psychological view. Comput. Hum. Behav. 28(1), 207–216 (2012) Google Scholar 15. Taneja, A., Arora, A.: Cross domain recommendation using multidimensional tensor factorization. Expert Syst. Appl. 92(1), 304–316 (2018) Google Scholar 16. Frolov, E., Oseledets, I.: Tensor methods and recommender systems. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 7(3), 1–12 (2017) Google Scholar 17. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009) MathSciNetzbMATHGoogle Scholar 18. Panniello, U., Tuzhilin, A., Gorgoglione, M.: Comparing context-aware recommender systems in terms of accuracy and diversity. User Model. User-Adap. Inter. 24(2), 35–65 (2014) Google Scholar 19. Rafailidis, D., Kefalas, P., Manolopoulos, Y.: Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst. Appl. 74(1), 11–18 (2017) Google Scholar 20. Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010) Google Scholar 21. Zheng, C., Haihong, E., Song, M., Song, J.: CMPTF: contextual modeling probabilistic tensor factorization for recommender systems. Neurocomputing 205(1), 141–151 (2016) Google Scholar 22. Hidasi, B., Tikk, D.: Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 67–82. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_5 Google Scholar 23. Lee, J., Lee, D., Lee, Y.C., Hwang, W.S., Kim, S.W.: Improving the accuracy of top-n recommendation using a preference model. Inf. Sci. 348(1), 290–304 (2016) Google Scholar 24. Vásquez, C., et al.: Cluster of the latin american universities top100 according to webometrics 2017. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 1–12. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_26 Google Scholar 25. Torres-Samuel, M., Vásquez, C.L., Viloria, A., Varela, N., Hernández-Fernandez, L., Portillo-Medina, R.: Analysis of patterns in the university world rankings webometrics, Shanghai, QS and SIR-SCimago: case Latin America. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 188–199. 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