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Managing Natural Noise in Recommender Systems

  • Luis Martínez
  • Jorge Castro
  • Raciel Yera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10071)

Abstract

E-commerce customers demand quick and easy access to suitable products in large purchase spaces. To support and facilitate this purchasing process to users, recommender systems (RSs) help them to find out the information that best fits their preferences and needs in an overloaded search space. These systems require the elicitation of customers’ preferences. However, this elicitation process is not always precise either correct because of external factors such as human errors, uncertainty, human beings inherent inconsistency and so on. Such a problem in RSs is known as natural noise (NN) and can negatively bias recommendations, which leads to poor user’s experience. Different proposals have been presented to deal with natural noise in RSs. Several of them require additional interaction with customers. Others just remove noisy information. Recently, new NN approaches dealing with the ratings stored in the user/item rating matrix have raised to deal with NN in a better and simpler way. This contribution is devoted to provide a brief review of the latter approaches revising crisp and fuzzy approaches for dealing with NN in RSs. Eventually it points out as a future research the management of NN in other recommendation scenarios as group RSs.

Keywords

Recommender systems Natural noise Fuzzy logic Computing with words Group recommender systems 

Notes

Acknowledgments

This research work was partially supported by the Research Project TIN2015-66524-P, and the Spanish Ministry of Education, Culture and Sport FPU fellowship (FPU13/01151).

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.T.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Amatriain, X., Jaimes, A., Oliver, N., Pujol, J.M.: Data mining methods for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 39–71. Springer, New York (2011)CrossRefGoogle Scholar
  3. 3.
    Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., Oliver, N.: The wisdom of the few: a collaborative filtering approach based on expert opinions from the web. In: Proceedings of the 32nd International ACM SIGIR Conference, pp. 532–539. ACM, New York (2009)Google Scholar
  4. 4.
    Amatriain, X., Pujol, J.M., Oliver, N.: I Like It.. I Like It Not: evaluating user ratings noise in recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 247–258. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02247-0_24 CrossRefGoogle Scholar
  5. 5.
    Burke, R.: Hybrid recommender systems: survey and experiments. Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Castro, J., Rodríguez, R.M., Barranco, M.J.: Weighting of features in content-based filtering with entropy and dependence measures. Int. J. Comput. Intell. Syst. 7(1), 80–89 (2014)CrossRefGoogle Scholar
  7. 7.
    Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 150–157. ACM (2000)Google Scholar
  8. 8.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, New York (2011)CrossRefGoogle Scholar
  9. 9.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2011)CrossRefGoogle Scholar
  10. 10.
    Espinilla, M., Montero, J., Rodríguez, J.: Computational intelligence in decision making. Int. J. Comput. Intell. Syst. 7(SUPPL.1), 1–5 (2014)CrossRefGoogle Scholar
  11. 11.
    Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Gunes, I., Kaleli, C., Bilge, A., Polat, H.: Shilling attacks against recommender systems: a comprehensive survey. Artif. Intell. Rev. 42(4), 767–799 (2014)CrossRefGoogle Scholar
  13. 13.
    Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1–2), 101–123 (2012)CrossRefGoogle Scholar
  14. 14.
    Li, B., Chen, L., Zhu, X., Zhang, C.: Noisy but non-malicious user detection in social recommender systems. World Wide Web 16(5–6), 677–699 (2013)CrossRefGoogle Scholar
  15. 15.
    Martínez, L., Herrera, F.: An overview on the 2-tuple linguistic model for computing with words in decision making: extensions, applications and challenges. Inf. Sci. 207(1), 1–18 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Martínez, L., Barranco, M.J., Pérez, L.G., Espinilla, M.: A knowledge based recommender system with multigranular linguistic information. Int. J. Comput. Intell. Syst. 1(3), 225–236 (2008)CrossRefGoogle Scholar
  17. 17.
    Martínez, L., Pérez, L.G., Barranco, M.: A multigranular linguistic content-based recommendation model: research articles. Int. J. Intell. Syst. 22(5), 419–434 (2007)CrossRefGoogle Scholar
  18. 18.
    Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7(4), Article 23 (2007)Google Scholar
  19. 19.
    Noguera, J., Barranco, M., Segura, R., Martínez, L.: A mobile 3D GIS hybrid recommender system for tourism. Inf. Sci. 215, 37–52 (2012)CrossRefGoogle Scholar
  20. 20.
    O’Mahony, M.P., Hurley, N.J., Silvestre, G.: Detecting noise in recommender system databases. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, pp. 109–115. ACM (2006)Google Scholar
  21. 21.
    Pazzani, M., Billsus, D.: Content-based recommendation systems. Adaptive Web 4321, 325–341 (2007)CrossRefGoogle Scholar
  22. 22.
    Pham, H.X., Jung, J.J.: Preference-based user rating correction process for interactive recommendation systems. Multimedia Tools Appl. 65(1), 119–132 (2013)CrossRefGoogle Scholar
  23. 23.
    Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: Proceedings of the third ACM Conference on Recommender Systems, pp. 93–100. ACM (2009)Google Scholar
  24. 24.
    Quesada, F.J., Palomares, I., Martínez, L.: Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators. Appl. Soft Comput. 35, 873–887 (2015)CrossRefGoogle Scholar
  25. 25.
    Rodríguez, R., Espinilla, M., Sánchez, P., Martínez, L.: Using linguistic incomplete preference relations to cold start recommendations. Internet Res. 20(3), 296–315 (2010)CrossRefGoogle Scholar
  26. 26.
    Rodríguez, R.M., Labella, Á., Martínez, L.: An overview on fuzzy modelling of complex linguistic preferences in decision making. Int. J. Comput. Intell. Syst. 9, 81–94 (2016)CrossRefGoogle Scholar
  27. 27.
    Vozalis, M.G., Margaritis, K.G.: Using SVD and demographic data for the enhancement of generalized Collaborative Filtering. Inf. Sci. 177(15), 3017–3037 (2007)CrossRefGoogle Scholar
  28. 28.
    Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. Manage. Inf. Syst. Q. 31(1), 137–209 (2007)Google Scholar
  29. 29.
    Toledo, R.Y., Mota, Y.C.: An e-learning collaborative filtering approach to suggest problems to solve in programming online judges. Int. J. Distance Educ. Technol. 12(2), 51–65 (2014)CrossRefGoogle Scholar
  30. 30.
    Toledo, R.Y., Mota, Y.C., Martínez, L.: Correcting noisy ratings in collaborative recommender systems. Knowl. Based Syst. 76, 96–108 (2015)CrossRefGoogle Scholar
  31. 31.
    Toledo, R.Y., Castro, J., Martínez, L.: A fuzzy model for managing natural noise in recommender systems. Appl. Soft Comput. 40, 187–198 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Computer Science and A.I.University of GranadaGranadaSpain
  3. 3.Centre for Quantum Computing and Intelligent SystemsUniversity of Technology SydneyUltimoAustralia
  4. 4.University of Ciego de ÁvilaCiego de ÁvilaCuba

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