DOI QR코드

DOI QR Code

Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries

  • 투고 : 2017.06.06
  • 심사 : 2017.08.01
  • 발행 : 2017.08.31

초록

The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user's access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user's preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.

키워드

참고문헌

  1. J. Callan, A. Smeaton, M. Beaulieu, P. Borlund, P. Brusilovsky, M. Chalmers, et al., Personalisation and Recommender Systems in Digital Libraries (Joint NSF-EU DELOS Working Group report). Sophia Antipolis, France: ERCIM, 2008.
  2. M. A. Gonçalves, E. A. Fox, L. T. Watson, and N. A. Kipp, "Streams, structures, spaces, scenarios, societies (5s): a formal model for digital libraries," ACM Transactions on Information Systems, vol. 22, no. 2, pp. 270-312, 2004. https://doi.org/10.1145/984321.984325
  3. L. Ross and P. Sennyey, "The library is dead, long live the library! The practice of academic librarianship and the digital revolution," Journal of Academic Librarianship, vol. 34, no. 2, pp. 145-152, 2008. https://doi.org/10.1016/j.acalib.2007.12.006
  4. R. D. Montoya, "Boundary objects/boundary staff: supporting digital scholarship in academic libraries," Journal of Academic Librarianship, vol. 43, no. 3, pp. 216-223, 2017. https://doi.org/10.1016/j.acalib.2017.03.001
  5. G. Marchionini, "Research and development in digital libraries," 2000 [Online]. Available: http://ils.unc.edu/ -march/digital_library_R_and_D.html
  6. H. Chao, "Assessing the quality of academic libraries on the Web: the development and testing of criteria," Library & Information Science Research, vol. 24, no. 2, pp. 169-194, 2002. https://doi.org/10.1016/S0740-8188(02)00111-1
  7. A. Shannon, B. Riecan, E. Sotirova, K. Atanassov, P. Melo-Pinto, and T. Kim, "A generalized net model of university subjects rating with intuitionistic fuzzy estimations," in Proceedings of the 16th International Conference on Intuitionistic Fuzzy Sets, Sofia, Bulgaria, 2012, pp. 61-67.
  8. M. Kobayashi, and K. Takeda, "Information retrieval on the web," ACM Computing Surveys, vol. 32, no. 2, pp. 148-173, 2000.
  9. G. Meghabghab and A. Kandel, Search Engines, Link Analysis, and User's Web Behavior. Heidelberg: Springer, 2008.
  10. J. Serrano-Guerrero, E. Herrera-Viedma, J. A. Olivas, A. Cerezo, and F. P. Romero, "A Google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0," Information Sciences, vol. 181, no. 9, pp. 1503-1516, 2011. https://doi.org/10.1016/j.ins.2011.01.012
  11. S. Charlotte-Ahrens, Recommender Systems: Relevance in the Consumer Purchasing Process. Berlin: Epubli, 2011.
  12. R. Burke, A. Felfernig, and M. Goker, "Recommender systems: an overview," AI Magazine, vol. 32, no. 3, pp. 13- 18, 2011. https://doi.org/10.1609/aimag.v32i3.2361
  13. A. Tejeda-Lorente, C. Porcel, E. Peis, R. Sanz, and E. Herrera-Viedma, "A quality based recommender system to disseminate information in a University Digital Library," Information Science, vol. 261, pp. 52-69, 2014. https://doi.org/10.1016/j.ins.2013.10.036
  14. F. Mata, L. Martinez, and E. Herrera-Viedma, "An adaptive consensus support model for group decisionmaking problems in a multigranular fuzzy linguistic context," IEEE Transactions on Fuzzy Systems, vol. 17, no. 2, pp. 279-290, 2009. https://doi.org/10.1109/TFUZZ.2009.2013457
  15. J. A. Morente-Molinera, I. J. Perez, R. Urena, and E. Herrera-Viedma, "On multi-granular fuzzy linguistic modelling in group decision making problems: a systematic review and future trends," Knowledge-Based Systems, vol. 74, pp. 49-60, 2015.
  16. C. Porcel, J. Moreno, and E. Herrera-Viedma, "A multi-disciplinar recommender system to advice research resources in University Digital Libraries," Expert Systems with Applications, vol. 36, no. 10, pp. 12520-12528, 2009. https://doi.org/10.1016/j.eswa.2009.04.038
  17. C. Porcel and E. Herrera-Viedma, "Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in University Digital Libraries," Knowledge-Based Systems, vol. 23, no. 1, pp. 32-39, 2010. https://doi.org/10.1016/j.knosys.2009.07.007
  18. C. Porcel, J. Morales-del Castillo, M. Cobo, A. Ruiz, and E. Herrera-Viedma, "An improved recommender system to avoid the persistent information overload in a University Digital Library," Control and Cybernetics, vol. 39, no. 4, pp. 899-924, 2010.
  19. A. Tejeda-Lorente, C. Porcel, J. Bernabe-Moreno, and E. Herrera-Viedma, "REFORE: a recommender system for researchers based on bibliometrics," Applied Soft Computing, vol. 30, pp. 778-791, 2015. https://doi.org/10.1016/j.asoc.2015.02.024
  20. L. A. Zadeh. "The concept of a linguistic variable and its applications to approximate reasoning-I," Information Sciences, vol. 8, no. 3, pp. 199-249, 1975. https://doi.org/10.1016/0020-0255(75)90036-5
  21. L. A. Zadeh. "The concept of a linguistic variable and its applications to approximate reasoning-II," Information Sciences, vol. 8, no. 4, pp. 301-357, 1975. https://doi.org/10.1016/0020-0255(75)90046-8
  22. L. A. Zadeh. "The concept of a linguistic variable and its applications to approximate reasoning-III," Information Sciences, vol. 9, no. 1, pp. 43-80, 1975. https://doi.org/10.1016/0020-0255(75)90017-1
  23. F. Herrera and L. Martinez, "A 2-tuple fuzzy linguistic representation model for computing with words," IEEE Transactions on Fuzzy Systems, vol. 8, no. 6, pp. 746-752, 2000. https://doi.org/10.1109/91.890332
  24. F. Herrera and L. Martinez, "A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making," IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 31, no. 2, pp. 227-234, 2001. https://doi.org/10.1109/3477.915345
  25. S. Alonso, F. Cabrerizo, F. Chiclana, F. Herrera, and E. Herrera-Viedma, "Group decision-making with incomplete fuzzy linguistic preference relations," International Journal of Intelligent Systems, vol. 24, no. 2, pp. 201-222, 2009. https://doi.org/10.1002/int.20332
  26. S. Alonso, F. Chiclana, F. Herrera, E. Herrera-Viedma, J. Alcala-Fdez, and C. Porcel, "A consistency‐based procedure to estimate missing pairwise preference values," International Journal of Intelligent Systems, vol. 23, no. 2, pp. 155-175, 2008. https://doi.org/10.1002/int.20262
  27. L. Martinez, L. Perez, M. Barranco, and M. Espinilla, "Improving the effectiveness of knowledge based recommender systems using incomplete linguistic preference relations," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 16, no. supplement 2, pp. 33-56, 2008. https://doi.org/10.1142/S0218488508005479
  28. R. R. Korfhage, Information Storage and Retrieval. New York, NY: John Wiley & Sons, 1997.
  29. R. Burke, "Hybrid web Recommender Systems," in The Adaptive Web. Heidelberg: Springer, 2007, pp. 377-408.
  30. F. J. Cabrerizo, J. A. Morente-Molinera, I. J. Perez, J. Lopez-Gijon, and E. Herrera-Viedma, "A decision support system to develop a quality management in academic digital libraries," Information Sciences, vol. 323, pp. 48-58, 2015. https://doi.org/10.1016/j.ins.2015.06.022
  31. A. Garcia-Crespo, J. Gomez-Berbis, R. Colomo-Palacios, and F. Garcia-Sanchez, "Digital libraries and Web 3.0: the CallimachusDL approach," Computers in Human Behavior, vol. 27, no. 4, pp. 1424-1430, 2011. https://doi.org/10.1016/j.chb.2010.07.046
  32. S. Hwang, W. Yang, and K. Ting, "Automatic index construction for multimedia digital libraries," Information Processing and Management, vol. 46, no. 3, pp. 295-307, 2010. https://doi.org/10.1016/j.ipm.2009.10.006
  33. M. Franke, A. Geyer-Schulz, and A. Neumann, "Recommender services in scientific digital libraries," in Multimedia Services in Intelligent Environments. Heidelberg: Springer, 2008, pp. 377-417.

피인용 문헌

  1. Multicriteria Decision Making Based on Generalized Maclaurin Symmetric Means with Multi-Hesitant Fuzzy Linguistic Information vol.10, pp.4, 2018, https://doi.org/10.3390/sym10040081