Advertisement

An Evidential Collaborative Filtering Dealing with Sparsity Problem and Data Imperfections

  • Raoua AbdelkhalekEmail author
  • Imen BoukhrisEmail author
  • Zied ElouediEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

One of the most promising approaches commonly used in Recommender Systems (RSs) is Collaborative Filtering (CF). It relies on a matrix of user-item ratings and makes use of past users’ ratings to generate predictions. Nonetheless, a large amount of ratings in the typical user-item matrix may be unavailable. The insufficiency of available rating data is referred to as the sparsity problem, one of the major issues that limit the quality of recommendations and the applicability of CF. Generally, the final predictions are represented as a certain rating score. This does not reflect the reality which is related to uncertainty and imprecision by nature. Dealing with data imperfections is another fundamental challenge in RSs allowing more reliable and intelligible predictions. Thereupon, we propose in this paper a Collaborative Filtering system that not only tackles the sparsity problem but also deals with data imperfections using the belief function theory.

Keywords

Recommender Systems Collaborative Filtering User-based Item-based Sparsity Belief function theory Uncertainty 

References

  1. 1.
    Ricci, F., Rokach, L., Shapira, B.: Recommender systems: Introduction and challenges. In: Recommender Systems Handbook, pp. 1–34. Springer (2015)Google Scholar
  2. 2.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence, pp. 1–19. Hindawi Publishing Corporation (2009)Google Scholar
  3. 3.
    Smets, P.: The transferable belief model for quantified belief representation. In: Quantified Representation of Uncertainty and Imprecision, pp. 267–301. Springer (1998)Google Scholar
  4. 4.
    Dempster, A.P.: A generalization of Bayesian inference. J. Roy. Stat. Soc.: Ser. B (Methodol.) 30, 205–247 (1968)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  6. 6.
    Xue, G.R., Lin, C., Yang, Q., Xi, W., Zeng, H.J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 114–121. ACM (2005)Google Scholar
  7. 7.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4, 133–151 (2001)CrossRefGoogle Scholar
  8. 8.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system-a case study. Minnesota Univ Minneapolis (2000)Google Scholar
  9. 9.
    Lian, J., Zhang, F., Xie, X., Sun, G.: CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. In: International Conference on World Wide Web Companion, pp. 817–818. World Wide Web (2017)Google Scholar
  10. 10.
    Abdelkhalek, R., Boukhris, I., Elouedi, Z.: Evidential item-based collaborative filtering. In: International Conference on Knowledge Science, Engineering and Management, pp. 628–639. Springer (2016)Google Scholar
  11. 11.
    Abdelkhalek, R., Boukhris, I., Elouedi, Z.: Assessing items reliability for collaborative filtering within the belief function framework. In: International Conference on Digital Economy, pp. 208–217. Springer (2017)Google Scholar
  12. 12.
    Abdelkhalek, R., Boukhris, I., Elouedi, Z.: A new user-based collaborative filtering under the belief function theory. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 315–324. Springer (2017)Google Scholar
  13. 13.
    Abdelkhalek, R., Boukhris, I., Elouedi, Z.: Towards a hybrid user and item-based collaborative filtering under the belief function theory. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 395–406. Springer (2018)Google Scholar
  14. 14.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  15. 15.
    Denoeux, T.: A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25, 804–813 (1995)CrossRefGoogle Scholar
  16. 16.
    Su, X., Khoshgoftaar, T.M.: Collaborative filtering for multi-class data using Bayesian networks. Int. J. Artif. Intell. Tools 17, 71–85 (2008)CrossRefGoogle Scholar
  17. 17.
    Elouedi, Z., Mellouli, K., Smets, P.: Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34, 782–787 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LARODEC, Institut Supérieur de Gestion de TunisUniversité de TunisTunisTunisia

Personalised recommendations