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Grouping Like-Minded Users for Ratings’ Prediction

  • Soufiene JaffaliEmail author
  • Salma Jamoussi
  • Abdelmajid Ben Hamadou
  • Kamel Smaili
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

Regarding the huge amount of products, sites, information, etc., finding the appropriate need of a user is a very important task. Recommendation Systems (RS) guide users in a personalized way to objects of interest within a large space of possible options. This paper presents an algorithm for recommending movies. We break the recommendation task into two steps: (1) Grouping Like-Minded users, and (2) create model for each group to predict user-movie ratings. In the first step we use the Principal Component Analysis to retrieve latent groups of similar users. In the second step, we employ three different regression algorithms to build models and predict ratings. We evaluate our results against the SVD++ algorithm and validate the results by employing the MAE and RMSE measures. The obtained results show that the algorithm presented gives an improvement in the MAE and the RMSE of about 0.42 and 0.5201 respectively.

Keywords

Rating prediction Social recommendation Grouping like-minded users 

References

  1. 1.
    Blei, D.M., Andrew, Y., Ng., Jordan, M.I., Lafferty, J.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 2003 (2003)Google Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithm for collaborative filtering. In: Proceedings of the 14th Conference on UAI, pp. 43–52 (1998)Google Scholar
  3. 3.
    Burke, R.: The Adaptive Web, pp. 377–408. Springer, Heidelberg (2007)Google Scholar
  4. 4.
    Fouss, F., Pirotte, A., Renders, J.M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)Google Scholar
  5. 5.
    Golbeck, J., Hendler, J.: Filmtrust: movie recommendations using trust in web-based social networks. In: CCNC 2006. 3rd IEEE, vol. 1, pp. 282–286Google Scholar
  6. 6.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retrieval 4(2), 133–151 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: Gumo -the general user model ontology. In: User Modeling (2005)Google Scholar
  8. 8.
    Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 89–115 (2004)Google Scholar
  9. 9.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989). JulyCrossRefGoogle Scholar
  10. 10.
    Jaffali, S., Ameur, H., Jamoussi, S., Ben Hamadou, A.: Glio: a new method for grouping like-minded users. In: Transactions on Computational Collective Intelligence XVIII. LNCS, vol. 9240, pp. 44–66. Springer, Heidelberg (2015)Google Scholar
  11. 11.
    Jaffali, S., Jamoussi, S.: Principal component analysis neural network for textual document categorization and dimension reduction. In: 6th International Conference on SETIT, pp. 835–839 (2012)Google Scholar
  12. 12.
    Khabbaz, M., Lakshmanan, L.V.S.: Toprecs: top-k algorithms for item-based collaborative filtering. In: Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT ’11, pp. 213–224. ACM (2011)Google Scholar
  13. 13.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD, pp. 426–434. ACM (2008)Google Scholar
  14. 14.
    Koren, Y.: The bellkor solution to the netflix grand prize. Netflix prize documentation (2009)Google Scholar
  15. 15.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 77–118. Springer, US (2015)Google Scholar
  16. 16.
    Kumar, R., Verma, B.K., Rastogi, S.S.: Social popularity based SVD++ recommender system. Int. J. Comput. Appl. 33–37 (2014)Google Scholar
  17. 17.
    Lu, Z., Shen, H.: A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm. In: Proceedings of the 19th International Database Engineering and Applications Symposium, Japan, pp. 72–80 (2015)Google Scholar
  18. 18.
    Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings KDD Cup Workshop at SIGKDD’07, pp. 39–42 (2007)Google Scholar
  19. 19.
    Quinlan, J.R.: Learning with continuous classes. In: Proceedings of the Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific (1992)Google Scholar
  20. 20.
    Raîche, G., Walls, T.A., Magis, D., Riopel, M., Blais, J.: Non-graphical solutions for cattells scree test. Methodol.: Eur. J. Res. Methods Behav. Soc. Sci. 9(1), 23–29 (2013)Google Scholar
  21. 21.
    Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, ICML ’07, pp. 791–798, New York, NY, USA. ACM (2007)Google Scholar
  22. 22.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender system—a case study. In: ACM WebKDD Workshop (2000)Google Scholar
  23. 23.
    Sch\(\ddot{o}\)lkopf, B., Smola, Williamson, A.J., R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)Google Scholar
  24. 24.
    Vapnik, V.N.: Statistical Learning Theory. Wiley (1998)Google Scholar
  25. 25.
    Yang, X., Liu, Y., Guo, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Soufiene Jaffali
    • 1
    Email author
  • Salma Jamoussi
    • 1
  • Abdelmajid Ben Hamadou
    • 1
  • Kamel Smaili
    • 2
  1. 1.MIRACL LaboratoryHigher Institute of Computer Science and Multimedia, University of SfaxSfaxTunisia
  2. 2.Campus Scientifique LORIANancyFrance

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