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International Conference on Theory and Practice of Natural Computing

TPNC 2016: Theory and Practice of Natural Computing pp 3–17Cite as

Managing Natural Noise in Recommender Systems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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References

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  5. Burke, R.: Hybrid recommender systems: survey and experiments. Adapted Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  9. Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2011)

    Article  Google Scholar 

  10. Espinilla, M., Montero, J., Rodríguez, J.: Computational intelligence in decision making. Int. J. Comput. Intell. Syst. 7(SUPPL.1), 1–5 (2014)

    Article  Google Scholar 

  11. Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  13. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1–2), 101–123 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Noguera, J., Barranco, M., Segura, R., Martínez, L.: A mobile 3D GIS hybrid recommender system for tourism. Inf. Sci. 215, 37–52 (2012)

    Article  Google Scholar 

  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. Pazzani, M., Billsus, D.: Content-based recommendation systems. Adaptive Web 4321, 325–341 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  30. Toledo, R.Y., Mota, Y.C., Martínez, L.: Correcting noisy ratings in collaborative recommender systems. Knowl. Based Syst. 76, 96–108 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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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).

Author information

Authors and Affiliations

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

    Luis Martínez

  2. Department of Computer Science and A.I., University of Granada, Granada, Spain

    Jorge Castro

  3. Centre for Quantum Computing and Intelligent Systems, University of Technology Sydney, Ultimo, Australia

    Jorge Castro

  4. University of Ciego de Ávila, Ciego de Ávila, Cuba

    Raciel Yera

Authors
  1. Luis Martínez
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  2. Jorge Castro
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  3. Raciel Yera
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Corresponding author

Correspondence to Luis Martínez .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  2. Tohoku University, Sendai, Japan

    Takaaki Mizuki

  3. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

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Martínez, L., Castro, J., Yera, R. (2016). Managing Natural Noise in Recommender Systems. In: Martín-Vide, C., Mizuki, T., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2016. Lecture Notes in Computer Science(), vol 10071. Springer, Cham. https://doi.org/10.1007/978-3-319-49001-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-49001-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49000-7

  • Online ISBN: 978-3-319-49001-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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