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Two new collaborative filtering approaches to solve the sparsity problem

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Abstract

Collaborative filtering which is the most successful technique of the Recommender System, has recently attracted great attention, especially in the field of e-commerce. CF is used to help users find their preferred items by assessing the preferences of other users to find most similar to the active one. Sparse datasets defend the efficiency of CF. Therefore this paper proposes two new methods that use the information provided via user ratings to overcome the sparsity problem without any change of dimension. The methods are implemented via Map-Reduce clustering-based CF. The proposed approaches have been tested by Movielens 100K, Movielens 1M, Movielens 20M, and Jester datasets in order to make a comparison with the traditional techniques. The experimental results show that the proposed methods can lead to improved performance of the Recommender System.

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References

  1. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: An Introduction to Recommender Systems. Cambridge University Press, New York (2011)

    Google Scholar 

  2. Dara, S., Chowdary, C.R., Kumar, C.: A survey on group recommender systems. J. Intell. Inf. Syst. 54, 271–295 (2020)

    Google Scholar 

  3. Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)

    Google Scholar 

  4. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowledge-Based Syst. 46, 109–132 (2013)

    Google Scholar 

  5. Jiang, Z., Gao, S.: An intelligent recommendation approach for online advertising based on hybrid deep neural network and parallel computing. Cluster Comput. (2019). https://doi.org/10.1007/s10586-019-02959-5

    Article  Google Scholar 

  6. Margaris, D., Vasilopoulos, D., Vassilakis, C.: Improving collaborative filtering’s rating prediction coverage in sparse datasets through the introduction of virtual near neighbors. In: 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8. IEEE (2019)

  7. Koohi, H., Kiani, K.: User based collaborative filtering using fuzzy C-means. Measurement 91(May), 134–139 (2016)

    Google Scholar 

  8. Koohi, H., Kiani, K.: A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst. Appl. 83, 30–39 (2017)

    Google Scholar 

  9. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide, vol. 1, pp. 285–295. (2001)

  10. Ghazarian, S., Nematbakhsh, M.A.: Enhancing memory-based collaborative filtering for group recommender systems. Expert Syst. Appl. 42(7), 3801–3812 (2015)

    Google Scholar 

  11. Ramezani, M., Moradi, P., Akhlaghian, F.: A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Phys. A 408, 72–84 (2014)

    Google Scholar 

  12. Bilge, A., Polat, H.: A comparison of clustering-based privacy-preserving collaborative filtering schemes. Appl. Soft Comput. 13(5), 2478–2489 (2013)

    Google Scholar 

  13. Zhou, J., Luo, T.: A novel approach to solve the sparsity problem in collaborative filtering. In: 2010 International Conference on Networking, Sensing and Control, pp. 165–170 (2010)

  14. Wang, J., Ke, L.: Feature subspace transfer for collaborative filtering. Neurocomputing 136, 1–6 (2014)

    Google Scholar 

  15. Singh, M.: Scalability and sparsity issues in recommender datasets: a survey. Knowl. Inf. Syst. 62(1), 1–43 (2020)

    Google Scholar 

  16. Baltrunas, L., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, pp. 119–126. (2010)

  17. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithm for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52. (1998)

  18. Su, X., Khoshgoftaar, T.M., Zhu, X., Greiner, R.: Imputation-boosted collaborative filtering using machine learning classifiers. In: Proceedings of the 2008 ACM Symposium on Applied Computing—SAC ’08, no. 2, p. 949. (2008)

  19. Hu, R., Dou, W., Liu, J.: ClubCF: a clustering-based collaborative filtering approach for big data application. IEEE Trans. Emerg. Top. Comput. 6750(c), 1–12 (2014)

    Google Scholar 

  20. Xu, R., Wang, S., Zheng, X., Chen, Y.: Distributed collaborative filtering with singular ratings for large scale recommendation. J. Syst. Softw. 95, 231–241 (2014)

    Google Scholar 

  21. Eesayo, P., Nald, I.X., Williams, C.: Principles of Big Data, no. 615. Elsevier, Amsterdam (2013)

    Google Scholar 

  22. Minelli, M., Chambers, M., Dhiraj, A.: Big Data, Big Analytics. Wiley, Hoboken (2013)

    Google Scholar 

  23. Dean, J.: Big Data, Data Mining and Machine Learning, vol. 41. Wiley, Hoboken (2014)

    Google Scholar 

  24. Jeong, H., Park, B., Park, M., Kiseok, K.K.: Big data and rule-based recommendation system in internet of things. Cluster Comput. 22, 1837–1846 (2019)

    Google Scholar 

  25. Tsai, C.F., Hung, C.: Cluster ensembles in collaborative filtering recommendation. Appl. Soft Comput. 12(4), 1417–1425 (2012)

    Google Scholar 

  26. Birtolo, C., Ronca, D.: Advances in clustering collaborative filtering by means of fuzzy C-means and trust. Expert Syst. Appl. 40(17), 6997–7009 (2013)

    Google Scholar 

  27. Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39, 10059–10072 (2012)

    Google Scholar 

  28. Oh, Y., Kim, Y.: A resource recommendation method based on dynamic cluster analysis of application characteristics. Cluster Comput. 22, 175–184 (2019)

    Google Scholar 

  29. Wang, Z., Yu, X., Feng, N., Wang, Z.: An improved collaborative movie recommendation system using computational intelligence. Vis. Lang. Comput. 25, 667–675 (2014)

    Google Scholar 

  30. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an Information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Google Scholar 

  31. Resnick, P., Varian, H.R., Editors, G.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Google Scholar 

  32. Choi, S.-M., Ko, S.-K., Han, Y.-S.: A movie recommendation algorithm based on genre correlations. Expert Syst. Appl. 39(9), 8079–8085 (2012)

    Google Scholar 

  33. Li, Q., Myaeng, S.H., Kim, B.M.: A probabilistic music recommender considering user opinions and audio features. Inf. Process. Manag. 43(2), 473–487 (2007)

    Google Scholar 

  34. Cleger-Tamayo, S., Fernández-Luna, J.M., Huete, J.F.: Top-N news recommendations in digital newspapers. Knowledge-Based Syst. 27, 180–189 (2012)

    Google Scholar 

  35. Leino, J.: User factors in recommender systems: case studies in e-commerce, news recommending, and e-learning, Tampere (2014)

  36. Garcia, I., Sebastia, L., Onaindia, E.: On the design of individual and group recommender systems for tourism. Expert Syst. Appl. 38(6), 7683–7692 (2011)

    Google Scholar 

  37. Nilashi, M., Bin Ibrahim, O., Ithnin, N., Sarmin, N.H.: A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electron. Commer. Res. Appl. 14(6), 542–562 (2015)

    Google Scholar 

  38. Symeonidis, P., Ntempos, D., Manolopoulos, Y.: Recommender Systems for Location-Based Social Networks. Springer, New York (2014)

    Google Scholar 

  39. Sun, Z., et al.: Recommender systems based on social networks. J. Syst. Softw. 99, 109–119 (2015)

    Google Scholar 

  40. Agarwal, N., Haque, E., Liu, H., Parsons, L.: Research paper recommender systems: a subspace clustering approach. Adv. Web-Age Inf. Manag. 3739, 475–491 (2005)

    Google Scholar 

  41. Chi, Y., Zhu, J., Huag, L., Xu, H.: Concepts recommendation for searching scientific papers. Cluster Comput. 22, 8669–8675 (2019)

    Google Scholar 

  42. Chen, L.C., Kuo, P.J., Liao, I.E.: Ontology-based library recommender system using MapReduce. Cluster Comput. 18(1), 113–121 (2015)

    Google Scholar 

  43. Li, L.: Next Generation of Recommender Systems: Algorithms and Applications. Dissertation, Digital Commons, Berkeley, CA, USA (2014)

    Google Scholar 

  44. Bhavithra, J., Saradha, A.: Personalized web page recommendation using case-based clustering and weighted association rule mining. Cluster Comput. 22, 6991–7002 (2019)

    Google Scholar 

  45. Wang, T., Manogaran, G., Wang, M.: Framework for social tag recommendation using Lion Optimization Algorithm and collaborative filtering techniques. Cluster Comput. (2019). https://doi.org/10.1007/s10586-019-02980-8

    Article  Google Scholar 

  46. He, F., Wei, P.: Research on comprehensive point of interest (POI) recommendation based on spark. Cluster Comput. 22, 9049–9057 (2019)

    Google Scholar 

  47. Almazro, D., Shahatah, G., Albdulkarim, L., Kherees, M., Martinez, R., Nzoukou, W.: A Survey Paper on Recommender Systems, arXiv:1006.5278 (2010)

  48. Barzegar Nozari, R., Koohi, H., Mahmodi, E.: A novel trust computation method based on user ratings to improve the recommendation. Int. J. Eng. 33(3), 377–386 (2020)

    Google Scholar 

  49. Guo, X.S.L.Y.: Gauss-core extension dependent prediction algorithm for collaborative filtering recommendation. Cluster Comput. 22, 11501–11511 (2019)

    Google Scholar 

  50. Cao, Y., Li, W., Zheng, D.: A hybrid recommendation approach using LDA and probabilistic matrix factorization. Cluster Comput. 22, 8811–8821 (2019)

    Google Scholar 

  51. Liu, X.: A collaborative filtering recommendation algorithm based on the influence sets of e-learning group’s behavior. Cluster Comput. 22, 2823–2833 (2019)

    Google Scholar 

  52. Ericson, K., Pallickara, S.: On the performance of high dimensional data clustering and classification algorithms. Futur. Gener. Comput. Syst. 29, 1024–1034 (2012)

    Google Scholar 

  53. Philip Chen, C.L., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. (Ny) 275, 314–347 (2014)

    Google Scholar 

  54. Lee, C.-R., Chang, Y.-F.: Enhancing accuracy and performance of collaborative filtering algorithm by stochastic SVD and its MapReduce implementation. In: 2013 IEEE International Symposium Parallel & Distributed Processing Workshops and PhD Forum, pp. 1869–1878. (2013)

  55. Pandey, S., Kumar, T.S.: Costomization of Recommendation System Using Collaborative Filtering Algorithm on Cloud Using Mahout, pp. 39–43. Springer, Cham (2014)

    Google Scholar 

  56. Shang, M.: User-based Collaborative-Filtering Recommendation Algorithms on Hadoop, pp. 478–481. IEEE, New York (2010)

    Google Scholar 

  57. Zhang, Z., Zhang, Y., Ren, Y.: Employing neighborhood reduction for alleviating sparsity and cold start problems in user—based collaborative filtering. Inf. Retr. J. 23, 449–472 (2020)

    Google Scholar 

  58. Zhao, J., Chen, N., Zhou, Z.: A temporal sparse collaborative appearance model for visual tracking. Multimed. Tools Appl. 79, 14103–14125 (2020)

    Google Scholar 

  59. Zhang, Z.: Sparsity, Robustness, and Diversification of Recommender Systems. Princeton University, Princeton (2014)

    Google Scholar 

  60. Dery, L.N., Kalech, M.: Iterative voting under uncertainty for group recommender systems. In: Proceedings of the Fourth ACM Conference on Recommender Systems. ACM, pp. 265–268. (2010)

  61. Xue, G.-R., et al.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR ’05, p. 114. (2005)

  62. Cheng, L., Wang, H.: A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl. Soft Comput. 18, 290–301 (2014)

    Google Scholar 

  63. Boratto, L.: Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system. In: Proceedings of the 16th International Conference on Enterprise Information Systems, no. JANUARY, pp. 564–572. (2014)

  64. Xu, Y., Yin, W., Wen, Z., Zhang, Y.: An alternating direction algorithm for matrix completion with nonnegative factors. Front. Math. China 7(2), 365–384 (2012)

    MathSciNet  MATH  Google Scholar 

  65. Hernando, A., Bobadilla, J., Ortega, F.: A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowledge-Based Syst. 97, 188–202 (2016)

    Google Scholar 

  66. Zhang, D., Hsu, C.-H., Chen, Q., Lloret, J., Vasilakos, A.: Cold-start recommendation using Bi-clustering and fusion for social recommender systems. IEEE Trans. Emerg. Top. Comput. 2(2), 239–250 (2013)

    Google Scholar 

  67. Al-shamri, M.Y.H.: Power coefficient as a similarity measure for memory-based collaborative recommender systems. Expert Syst. Appl. 41, 5680–5688 (2014)

    Google Scholar 

  68. Bobadilla, J., Ortega, F., Hernando, A.: A collaborative filtering similarity measure based on singularities. Inf. Process. Manag. 48(2), 204–217 (2012)

    Google Scholar 

  69. Feng, W., Zhu, Q., Zhuang, J., Yu, S.: An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth. Cluster Comput. 22, 7401–7412 (2019)

    Google Scholar 

  70. Geetha, M.M.S., Selva, S.: Adaptable and proficient Hellinger Coefficient Based Collaborative Filtering for recommendation system. Cluster Comput. 22, 12325–12338 (2019)

    Google Scholar 

  71. Rana, C., Kumar, S.: An extended evolutionary clustering algorithm for an adaptive recommender system. Soc. Netw. Anal. Min. 4(164), 1–13 (2014)

    Google Scholar 

  72. Jameson, A., Smyth, B.: “Recommendation to groups. Adaptive Web, pp. 596–627. Springer, Berlin (2007)

    Google Scholar 

  73. Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Syst. 56(November), 156–166 (2014)

    Google Scholar 

  74. Comput, J.P.D., Kambatla, K., Kollias, G., Kumar, V., Grama, A.: Trends in big data analytics. J. Parallel Distrib. Comput. 74(7), 2561–2573 (2014)

    Google Scholar 

  75. Wu, X., Zhu, X., Wu, G., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Google Scholar 

  76. Sun, C., Gao, R., Xi, H.: Big data based retail recommender system of non E-commerce. In: Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on IEEE, pp. 1–7. (2014)

  77. Schröder, G., Thiele, M., Lehner, W.: Setting goals and choosing metrics for recommender system evaluations. In: UCERSTI2 Workshop at the 5th ACM Conference on Recommender Systems (2011)

  78. Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., Salehi, M.: Evaluating collaborative filtering recommender algorithms: a survey. IEEE Access 6, 74003–74024 (2018)

    Google Scholar 

  79. Silveira, T., Zhang, M., Lin, X., Liu, Y., Ma, S.: How good your recommender system is? A survey on evaluations in recommendation. Int. J. Mach. Learn. Cybern. 10(5), 813–831 (2017)

    Google Scholar 

  80. Aghdam, M.H., Analoui, M., Kabiri, P.: A novel non-negative matrix factorization method for recommender systems. Appl. Math. Inf. Sci. 9(5), 2721–2732 (2015)

    Google Scholar 

  81. Huang, T., Zhang, D., Bi, L.: Neural embedding collaborative filtering for recommender systems. Neural Comput. Appl. (2020). https://doi.org/10.1007/s00521-020-04920-9

    Article  Google Scholar 

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  1. Electrical and Computer Engineering Department, Semnan University, Semnan, Iran

    Hamidreza Koohi & Kourosh Kiani

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  1. Hamidreza Koohi
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Koohi, H., Kiani, K. Two new collaborative filtering approaches to solve the sparsity problem. Cluster Comput 24, 753–765 (2021). https://doi.org/10.1007/s10586-020-03155-6

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