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Using Bibliometrics and Fuzzy Linguistic Modeling to Deal with Cold Start in Recommender Systems for Digital Libraries

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Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 643))

Abstract

Every recommender system approach suffers the cold start problem to a greater or lesser extent. To soften this impact, the more common solution is to find the way of populating users profiles either using hybrid approach or finding external data sources. In this paper, we present a fuzzy linguistic approach that using bibliometrics aids to soft or remove the necessity of interaction of users providing them with personalized profiles built beforehand, thus reducing the cold start problem. To prove the effectiveness of the system, we conduct a test involving some researchers, aiming to build their profiles automatically. The results obtained proved to be satisfactory for the researchers.

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References

  1. Bordogna, G., Pasi, G.: A fuzzy linguistic approach generalizing boolean information retrieval: A model and its evaluation. J. Am. Soc. Inf. Sci. 44(2), 70 (1993)

    Article  Google Scholar 

  2. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408 (2007)

    Google Scholar 

  3. Chen, P.Y., Wu, S.Y.: Does collaborative filtering technology impact sales? empirical evidence from amazon.com (2007). https://ssrn.com/abstract=1002698

  4. Claypool, M., Gokhale, A., Miranda, T.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR 1999 Workshop on Recommender Systems-Implementation and Evaluation, pp. 714–720 (1999)

    Google Scholar 

  5. Delgado, M., Herrera, F., Herrera-Viedma, E., Martinez, L.: Combining numerical and linguistic information in group decision making. Inf. Sci. 107(1–4), 177–194 (1998)

    Article  MathSciNet  Google Scholar 

  6. Edmunds, A., Morris, A.: The problem of information overload in business organizations: a review of the literature. Int. J. Inf. Manage. 20, 17–28 (2000)

    Article  Google Scholar 

  7. Esteban, B., Tejeda-Lorente, A., Porcel, C., Arroyo, M., Herrera-Viedma, E.: TPLUFIB-WEB: A fuzzy linguistic web system to help in the treatment of low back pain problems. Knowl. Based Syst. 67, 429–438 (2014)

    Article  Google Scholar 

  8. Good, N., Schafer, J., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, AAAI/IAAI, pp. 439–446 (1999)

    Google Scholar 

  9. Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Model. User-Adap. Inter. 11, 203–259 (2001)

    Article  MATH  Google Scholar 

  10. Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Article  Google Scholar 

  11. Herrera, F., Herrera-Viedma, E.: Choice functions and mechanisms for linguistic preference relations. Eur. J. Oper. Res. 120, 144–161 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  12. Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)

    Article  Google Scholar 

  13. Herrera, F., Martínez, L.: A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31(2), 227–234 (2001)

    Article  Google Scholar 

  14. Herrera-Viedma, E., López-Herrera, A., Luque, M., Porcel, C.: A fuzzy linguistic irs model based on a 2-tuple fuzzy linguistic approach. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 15(2), 225–250 (2007)

    Article  MATH  Google Scholar 

  15. Hsu, M.: A personalized english learning recommender system for ESL students. Expert Syst. Appl. 34, 377–408 (2008)

    Google Scholar 

  16. Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, pp. 3–24. IOS Press, Amsterdam, The Netherlands (2007)

    Google Scholar 

  17. Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4, Part 2), 2065–2073 (2014)

    Article  Google Scholar 

  18. Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl. Based Syst. 56, 156–166 (2014)

    Article  Google Scholar 

  19. Long-Sheng, C., Fei-Hao, H., Mu-Chen, C., Yuan-Chia, H.: Developing recommender systems with the consideration of product profitability for sellers. Inf. Sci. 178(4), 1032–1048 (2008)

    Article  Google Scholar 

  20. Lops, P., 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. 73–106. Springer (2011)

    Google Scholar 

  21. Rish, I.: An empirical study of the naive bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Vol. 3, pp. 41–46. IBM New York (2001)

    Google Scholar 

  22. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253–260. ACM (2002)

    Google Scholar 

  23. Son, L.H.: Dealing with the new user cold-start problem in recommender systems: A comparative review. Inf. Syst. 58, 87–104 (2016)

    Article  Google Scholar 

  24. Tejeda-Lorente, A., Porcel, C., Bernabé-Moreno, J., Herrera-Viedma, E.: Refore: A recommender system for researchers based on bibliometrics. Appl. Soft Comput. 30, 778–791 (2015)

    Article  Google Scholar 

  25. Tejeda-Lorente, A., Porcel, C., Peis, E., Sanz, R., Herrera-Viedma, E.: A quality based recommender system to disseminate information in a university digital library. Inf. Sci. 261, 52–69 (2014)

    Article  Google Scholar 

  26. Zadeh, L.: The concept of a linguistic variable and its applications to approximate reasoning. Part I, Inf. Sci. 8, 199–249 (1975), Part II, Inf. Sci. 8, 301–357 (1975), Part III. Inf. Sci. 9(1975), 43–80 (1975)

    Google Scholar 

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Acknowledgments

This paper has been developed with the FEDER financing under Projects TIN2013-40658-P and TIN2016-75850-R

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Alvaro Tejeda-Lorente, Juan Bernabé-Moreno & Enrique Herrera-Viedma

  2. Department of Computer Science, University of Jaén, Jaén, Spain

    Carlos Porcel

Authors
  1. Alvaro Tejeda-Lorente
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  2. Juan Bernabé-Moreno
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  3. Carlos Porcel
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  4. Enrique Herrera-Viedma
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Corresponding author

Correspondence to Alvaro Tejeda-Lorente .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Slawomir Zadrożny

  4. Institute of Biophysics and Biomedical Engineering, Department of Bioinformatics and Mathematical Modelling, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

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Tejeda-Lorente, A., Bernabé-Moreno, J., Porcel, C., Herrera-Viedma, E. (2018). Using Bibliometrics and Fuzzy Linguistic Modeling to Deal with Cold Start in Recommender Systems for Digital Libraries. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-319-66827-7_36

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  • DOI: https://doi.org/10.1007/978-3-319-66827-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66826-0

  • Online ISBN: 978-3-319-66827-7

  • eBook Packages: EngineeringEngineering (R0)

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