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Joining Items Clustering and Users Clustering for Evidential Collaborative Filtering

  • Raoua AbdelkhalekEmail author
  • Imen BoukhrisEmail author
  • Zied ElouediEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

Abstract

Recommender Systems (RSs) are supporting users to cope with the flood of information. Collaborative Filtering (CF) is one of the most well-known approaches that have achieved a widespread success in RSs. It consists in picking out the most similar users or the most similar items to provide recommendations. Clustering techniques can be adopted in CF for grouping these similar users or items into some clusters. Nevertheless, the uncertainty comprised throughout the clusters assignments as well as the final predictions should also be considered. Therefore, in this paper, we propose a CF recommendation approach that joins both users clustering strategy and items clustering strategy using the belief function theory. In our approach, we carry out an evidential clustering process to cluster both users and items based on past preferences and predictions are then performed accordingly. Joining users clustering and items clustering improves the scalability and the performance of the traditional neighborhood-based CF under an evidential framework.

Keywords

Recommender Systems Collaborative Filtering Uncertain reasoning Belief function theory Users clustering Items clustering 

References

  1. 1.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)CrossRefGoogle Scholar
  2. 2.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: International Conference on Computer and Information Technology, Dhaka, Bangladesh. IEEE (2002)Google Scholar
  3. 3.
    Dempster, A.P.: A generalization of Bayesian inference. J. Roy. Stat. Soc. Ser. B (Methodol.) 30, 205–247 (1968)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Shafer, G.: A Mathematical Theory of Evidence, 1st edn. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  5. 5.
    Smets, P.: The transferable belief model for quantified belief representation. In: Smets, P. (ed.) Quantified Representation of Uncertainty and Imprecision. HDRUMS, vol. 1, pp. 267–301. Springer, Dordrecht (1998).  https://doi.org/10.1007/978-94-017-1735-9_9CrossRefzbMATHGoogle Scholar
  6. 6.
    Masson, M.H., Denoeux, T.: ECM: an evidential version of the fuzzy c-means algorithm. Pattern Recogn. 41(4), 1384–1397 (2008)CrossRefGoogle Scholar
  7. 7.
    Denoeux, T.: A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25, 804–813 (1995)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., Lin, Y., Lin, M., Liu, J.: An effective collaborative filtering algorithm based on user preference clustering. Appl. Intell. 45(2), 230–240 (2016).  https://doi.org/10.1007/s10489-015-0756-9 CrossRefGoogle Scholar
  9. 9.
    Xue, G.R., et al.: Scalable collaborative filtering using cluster-based smoothing. In: ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 114–121. ACM (2005)Google Scholar
  10. 10.
    O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: ACM SIGIR Workshop on Recommender Systems, vol. 128. UC Berkeley (1999)Google Scholar
  11. 11.
    Abdelkhalek, R., Boukhris, I., Elouedi, Z.: Towards a hybrid user and item-based collaborative filtering under the belief function theory. In: Medina, J., et al. (eds.) IPMU 2018. CCIS, vol. 853, pp. 395–406. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91473-2_34CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    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 2019

Authors and Affiliations

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

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