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A Content Based e-Commerce Recommendation Approach Under the Veristic Framework

  • Imen BenzartiEmail author
  • Hafedh Mili
  • Amandine Paillard
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

Abstract

A recommendation system is an automated tool that suggests an ordered list of appropriate items to a user. In this paper, we propose a recommendation algorithm that takes into account the variable and complex semantics of multi-valued properties, and the level of uncertainty or fuzziness inherent in the representations of users and items. In particular, we will rely on the concept of veristic variables, proposed by Yager, and propose algorithms for building user profiles, and for matching product descriptions to those profiles. We tested our algorithms on the Movielens datasets. Our results show statistically significant improvements in the f1-score and the Root Mean Square Error (RMSE) compared to baselines like Bayesian and K-nearest neighbors (KNN) approaches.

Keywords

Recommendation algorithms Veristic variable Fuzzy sets Content based recommendation 

References

  1. 1.
    Benzarti, I., Mili, H.: A development framework for customer experience management applications: principles and case study. In: 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), pp. 118–125. IEEE (2017)Google Scholar
  2. 2.
    Costas, P., Nikos, I.K.: A comparative assessment of measures of similarity of fuzzy values. Fuzzy Sets Syst. 56(2), 171–174 (1993).  https://doi.org/10.1016/0165-0114(93)90141-4MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Dell’Agnello, D., Mencar, C., Fanelli, A.M.: Item recommendation with veristic and possibilistic metadata: a preliminary approach. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications, pp. 261–266. IEEE (2009)Google Scholar
  4. 4.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 230–237. Association for Computing Machinery, Inc. (1999)Google Scholar
  5. 5.
    Lee-Kwang, H., Song, Y.S., Lee, K.M.: Similarity measure between fuzzy sets and between elements. Fuzzy Sets Syst. 62(3), 291–293 (1994).  https://doi.org/10.1016/0165-0114(94)90113-9MathSciNetCrossRefGoogle Scholar
  6. 6.
    Manouselis, N., Vuorikari, R., Van Assche, F.: Simulated analysis of maut collaborative filtering for learning object recommendation. In: Proceedings of the 1st Workshop on Social Information Retrieval for Technology Enhanced Learning, vol. 307, pp. 27–35 (2007)Google Scholar
  7. 7.
    Mao, M., Lu, J., Zhang, G., Zhang, J.: A fuzzy content matching-based e-commerce recommendation approach. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2015)Google Scholar
  8. 8.
    Marin, L., Moreno, A., Isern, D.: Automatic preference learning on numeric and multi-valued categorical attributes. Knowl.-Based Syst. 56, 201–215 (2014)CrossRefGoogle Scholar
  9. 9.
    Mili, H., Benzarti, I., Meurs, M.J., Obaid, A., Gonzalez-Huerta, J., Haj-Salem, N., Boubaker, A.: Context aware customer experience management: A development framework based on ontologies and computational intelligence. In: Sentiment Analysis and Ontology Engineering, pp. 273–311. Springer (2016)Google Scholar
  10. 10.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195–204. ACM (2000)Google Scholar
  11. 11.
    Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook, pp. 37–76. Springer (2015)Google Scholar
  12. 12.
    Sala, A., Guerra, T.M., Babuška, R.: Perspectives of fuzzy systems and control. Fuzzy Sets Syst. (2005).  https://doi.org/10.1016/j.fss.2005.05.041MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Shang, S., Kulkarni, S.R., Cuff, P.W., Hui, P.: A randomwalk based model incorporating social information for recommendations. In: 2012 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6. IEEE (2012)Google Scholar
  14. 14.
    Shani, G., Gunawardana, A.: Tutorial on application-oriented evaluation of recommendation systems. AI Commun. 26(2), 225–236 (2013)MathSciNetGoogle Scholar
  15. 15.
    Tzvieli, A.: Possibility theory: an approach to computerized processing of uncertainty. J. Am. Soc. Inform. Sci. 41(2), 153–154 (1990)CrossRefGoogle Scholar
  16. 16.
    Walker, B.K.: The emergence of customer experience management solutions (2011)Google Scholar
  17. 17.
    Wu, I.C., Hwang, W.H.: A genre-based fuzzy inference approach for effective filtering of movies. Intell. Data Anal. 17(6), 1093–1113 (2013).  https://doi.org/10.3233/IDA-130622CrossRefGoogle Scholar
  18. 18.
    Yager, R.R.: Set-based representations of conjunctive and disjunctive knowledge. Inf. Sci. 41(1), 1–22 (1987)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yager, R.R.: Toward a theory of conjunctive variables. Int. J. Gen. Syst. 13(3), 203–227 (1987)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Yager, R.R.: Reasoning with conjunctive knowledge. Fuzzy Sets Syst. 28(1), 69–83 (1988)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yager, R.R.: Veristic variables. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 30(1), 71–84 (2000)CrossRefGoogle Scholar
  22. 22.
    Yager, R.R.: Fuzzy logic methods in recommender systems. Fuzzy Sets Syst. 136(2), 133–149 (2003).  https://doi.org/10.1016/S0165-0114(02)00223-3MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Yera, R., Martinez, L.: Fuzzy tools in recommender systems: a survey. Int. J. Comput. Intell. Syst. 10(1), 776–803 (2017)CrossRefGoogle Scholar
  24. 24.
    Younes, Z., Abdallah, F., Denœux, T.: Fuzzy multi-label learning under veristic variables. In: International Conference on Fuzzy Systems, pp. 1–8. IEEE (2010)Google Scholar
  25. 25.
    Zenebe, A., Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst. 160(1), 76–94 (2009).  https://doi.org/10.1016/j.fss.2008.03.017MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Zhang, R., Tran, T.: An information gain-based approach for recommending useful product reviews. Knowl. Inf. Syst. 26(3), 419–434 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LATECE LaboratoryUniversity of Quebec at MontrealMontrealCanada
  2. 2.University of MontpellierMontpellierFrance

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