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A Collaborative Filtering System Using Clustering and Genetic Algorithms

  • Soojung LeeEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1071)

Abstract

Recommender systems have been essential these days to assist online customers to acquire useful information. However, one of the popular types of the systems called memory-based collaborative filtering suffers from several fundamental problems in spite of its main advantages such as simplicity and efficiency. This study addresses the scalability problem which is one of major problems of the system. We employ a clustering technique to handle the problem and propose a novel idea using the genetic algorithm to enhance the performance of the system in terms of prediction accuracy, not to mention scalability. Experimental results demonstrated successful performance achievements of the proposed method under various data conditions.

Keywords

Collaborative filtering Recommender system Clustering Genetic algorithm 

References

  1. 1.
    Aamir, M., Bhusry, M.: Recommendation system: state of the art approach. Int. J. Comput. Appl. 120(12), 25–32 (2015)Google Scholar
  2. 2.
    Alhijawi, B., Kilani, Y.: Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems. In: The 15th IEEE/ACIS International Conference on Computer and Information Science, pp. 1–6 (2016)Google Scholar
  3. 3.
    Bobadilla, J., Ortega, F., Hernando, A., Alcal, J.: Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl.-Based Syst. 24(8), 1310–1316 (2011)CrossRefGoogle Scholar
  4. 4.
    Gong, S.: A collaborative filtering recommendation algorithm based on user clustering and item clustering. J. Sofw. 5(7), 745–752 (2010)Google Scholar
  5. 5.
    Kim, K.J., Ahn, H.: A recommender systems using GA K-means clustering in an online shopping market. Expert Syst. Appl. 34, 1200–1209 (2008)CrossRefGoogle Scholar
  6. 6.
    Lee, M., Choi, P., Woo, Y.: A hybrid recommender system combining collaborative filtering with neural network. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 531–534. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-47952-X_77CrossRefGoogle Scholar
  7. 7.
    Liao, C.L., Lee, S.J.: A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electron. Commerce Res. Appl. 18, 1–9 (2016)CrossRefGoogle Scholar
  8. 8.
    Nilashi, M., Jannach, D., bin Ibrahim, O., Ithnin, N.: Clustering- and regression-based multi-criteria collaborative filtering with incremental updates. Inf. Sci. 293, 235–250 (2015)Google Scholar
  9. 9.
    Purbey, N., Pawde, K., Gangan, S., Karani, R.: Using self-organizing maps for recommender systems. Int. J. Soft Comput. Eng. 4(5), 47–50 (2014)Google Scholar
  10. 10.
    Resnick, P., Lakovou, N., Sushak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM Press (1994)Google Scholar
  11. 11.
    Roh, T.H., Oh, K.J., Han, I.: The collaborative filtering recommendation based on SOM cluster-indexing CBR. Expert Syst. Appl. 25(3), 413–423 (2003)CrossRefGoogle Scholar
  12. 12.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: The Fifth International Conference on Computer and Information Technology (2002)Google Scholar
  13. 13.
    Shivhare, H., Gupta, A., Sharma, S.: Recommender system using fuzzy C-means clustering and genetic algorithm based weighted similarity measure. In: International Conference on Computer, Communication and Control (IC4), pp. 1–8. IEEE (2015)Google Scholar
  14. 14.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009 (2009) Google Scholar
  15. 15.
    Tsai, C.F., Hung, C.: Cluster ensembles in collaborative filtering recommendation. Appl. Soft Comput. 12, 1417–1425 (2012)CrossRefGoogle Scholar
  16. 16.
    Xue, G., Lin, C., Yang, Q., Xi, W., Zeng, H., Yu, Y.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings International Conference on Special Interest Group on Information Retrieval, pp. 114–121. ACM (2005)Google Scholar
  17. 17.
    Ye, H.: A personalized collaborative filtering recommendation using association rules mining and self-organizing map. J. Softw. 6(4), 732–739 (2011)Google Scholar
  18. 18.
    Zhang, F., Chang, H.Y.: A collaborative filtering algorithm employing genetic clustering to ameliorate the scalability issue. In: IEEE International Conference on e-Business Engineering, pp. 331–338 (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Gyeongin National University of EducationAnyangRepublic of Korea

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