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Enhancing the Sales Diversity Using a Two-Stage Improved KNN Algorithm

  • ChemsEddine BerbagueEmail author
  • Nour El islem Karabadji
  • Hassina Seridi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)

Abstract

In recommender systems field (RS), considering a commercial system perspective involves covering in an accurate manner most items available in the market. However, in the memory based collaborative filtering (CF),the recommendation ability is limited because of the huge size of users and items. For this reason, the clustering algorithms were employed to improve the scalability of the system by partitioning users data into clusters then performing computations on each cluster separately. We propose in this paper a recommendation approach that targets two well-known issues: the scalability problem and the recommendation diversity. Our contribution consists of two successive stages: a) K-nearest neighbor (KNN) algorithm based on the use of an adapted similarity measure. b) An adjusted neighborhood selection performed by a genetic algorithm. The approach aims to improve the quality of the neighborhood set by exploring the reduced search space obtained in the first step, to choose among them the best ones who can enhance the quality of the recommendations. The proposed algorithm was compared to baseline recommender systems and showed competitive results in terms of the diversity and the precision of the recommendations.

Keywords

Memory-based collaborative filtering Genetic algorithm Diversity 

References

  1. 1.
    Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRefGoogle Scholar
  2. 2.
    Park, Y.J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: Proceedings of the 2008 ACM Conference on Recommender Systems - RecSys 2008. ACM Press (2008)Google Scholar
  3. 3.
    Chen, L., Guo, H., Lv, H., Wu, S.: Intelligent recommendation algorithm based on hidden markov chain model. In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE (2016)Google Scholar
  4. 4.
    Cakir, O., Aras, M.E.: A recommendation engine by using association rules. Procedia Soc. Behav. Sci. 62, 452–456 (2012)CrossRefGoogle Scholar
  5. 5.
    Paradarami, T.K., Bastian, N.D., Wightman, J.L.: A hybrid recommender system using artificial neural networks. Expert Syst. Appl. 83, 300–313 (2017)CrossRefGoogle Scholar
  6. 6.
    Thiengburanathum, P., Cang, S., Yu, H.: A decision tree based recommendation system for tourists. In: 2015 21st International Conference on Automation and Computing (ICAC), IEEE (2015)Google Scholar
  7. 7.
    Liao, C.L., Lee, S.J.: A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electron. Commer. Res. Appl. 18, 1–9 (2016)CrossRefGoogle Scholar
  8. 8.
    Sundaresan, N.: Recommender systems at the long tail. In: Proceedings of the Fifth ACM Conference on Recommender Systems - RecSys 2011, ACM Press (2011)Google Scholar
  9. 9.
    Zhang, M., Hurley, N., Li, W., Xue, X.: A double-ranking strategy for long-tail product recommendation. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, IEEE (2012)Google Scholar
  10. 10.
    Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)CrossRefGoogle Scholar
  11. 11.
    Liu, R.R., Jia, C.X., Zhou, T., Sun, D., Wang, B.H.: Personal recommendation via modified collaborative filtering. Phys. A Stat. Mech. Appl. 388(4), 462–468 (2009)CrossRefGoogle Scholar
  12. 12.
    Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys 2017. ACM Press (2017)Google Scholar
  13. 13.
    Seyerlehner, K., Flexer, A., Widmer, G.: On the limitations of browsing top-n recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems - RecSys 2009. ACM Press (2009)Google Scholar
  14. 14.
    Alhijawi, B., Kilani, Y.: Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–6 (2016)Google Scholar
  15. 15.
    Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N.: Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems - RecSys 2012. ACM Press (2012)Google Scholar
  16. 16.
    Guimarães, A.P., Costa, T.F., Lacerda, A., Pappa, G.L., Ziviani, N.: GUARD: a genetic unified approach for recommendation. JIDM 4(3), 295–310 (2013)Google Scholar
  17. 17.
    Wang, S., Gong, M., Ma, L., Cai, Q., Jiao, L.: Decomposition based multiobjective evolutionary algorithm for collaborative filtering recommender systems. In: 2014 IEEE Congress on Evolutionary Computation (CEC), IEEE (2014)Google Scholar
  18. 18.
    Berbague, C., Karabadji, N.E.I., Seridi, H.: An evolutionary scheme for improving recommender system using clustering. In: CIIA. Volume 522 of IFIP. Advances in Information and Communication Technology, pp. 290–301. Springer (2018)Google Scholar
  19. 19.
    Verma, D.A., Virk, H.K.: A hybrid recommender system using genetic algorithm and knn approach (2015)Google Scholar
  20. 20.
    Imandoust, S.B., Bolandraftar, M.: Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background (2013)Google Scholar
  21. 21.
    Xie, S., Feng, Y.: A recommendation system combining LDA and collaborative filtering method for scenic spot. In: 2015 2nd International Conference on Information Science and Control Engineering, IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • ChemsEddine Berbague
    • 1
    Email author
  • Nour El islem Karabadji
    • 1
    • 2
  • Hassina Seridi
    • 1
  1. 1.Electronic Document Management Laboratory (LabGED)Badji Mokhtar-Annaba UniversityAnnabaAlgeria
  2. 2.Higher School of Industrial TechnologiesAnnabaAlgeria

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