Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

  • Jesús SilvaEmail author
  • Noel Varela
  • Omar Bonerge Pineda Lezama
  • Hugo Hernández-P
  • Jairo Martínez Ventura
  • Boris de la Hoz
  • Leidy Pérez Coronel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)


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.


Collaborative filtering Context aware recommendation system Contextual Modeling Item recommendations Multi-dimensionality Tensor Factorization 


  1. 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. 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. 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). Scholar
  4. 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). Scholar
  5. 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. 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. 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. 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. 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). Scholar
  10. 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. 11.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 4(1), 1–12 (2009)Google Scholar
  12. 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. 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. 14.
    Buder, J., Schwind, C.: Learning with personalized recommender systems: a psychological view. Comput. Hum. Behav. 28(1), 207–216 (2012)Google Scholar
  15. 15.
    Taneja, A., Arora, A.: Cross domain recommendation using multidimensional tensor factorization. Expert Syst. Appl. 92(1), 304–316 (2018)Google Scholar
  16. 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. 17.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetzbMATHGoogle Scholar
  18. 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. 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. 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. 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. 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). Scholar
  23. 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. 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). Scholar
  25. 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).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jesús Silva
    • 1
    Email author
  • Noel Varela
    • 2
  • Omar Bonerge Pineda Lezama
    • 3
  • Hugo Hernández-P
    • 4
  • Jairo Martínez Ventura
    • 4
  • Boris de la Hoz
    • 4
  • Leidy Pérez Coronel
    • 4
  1. 1.Universidad Peruana de Ciencias AplicadasLimaPerú
  2. 2.Universidad de la CostaBarranquillaColombia
  3. 3.Universidad Tecnológica Centroamericana (UNITEC)San Pedro SulaHonduras
  4. 4.Corporación Universitaria LatinoamericanaBarranquillaColombia

Personalised recommendations