Application of Machine Learning in Collaborative Filtering Recommender Systems

  • Authors

    • Rajesh Kumar Ojha
    • Dr. Bhagirathi Nayak
    2018-12-03
    https://doi.org/10.14419/ijet.v7i4.38.24445
  • Recommender Systems (RS), Collaborative filtering, k-Nearest Neighbors, Classification.

  • Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.

     

     

     

  • References

    1. [1] Q. Wang, M. Sun, X. Yuan, “Collaborative Filtering Recommendation Algorithm based on Hybrid User Modelâ€, FSKD, 2010.

      [2] D J.Bobadilla, F. Ortega, A. Hernando, A. Gutierrez, Recommender systems survey,Knowledge-Based Systems,46(2013) 109-132.

      [3] Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender Systems Handbook. Springer

      [4] P. Resnick, H.R. Varian, Recommender systems, Communications of the ACM, 40(1997) 56-58.

      [5] G. Zhuo, Jingyu Sun and Xueli Yu “A Framework for Multi- Type Recommendationsâ€, Eighth International Conference on Fuzzy Systems and Knowledge Discovery, 2007.

      [6] Deshpande M. and Karypis, G. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22, 1 (2004), 143-177.

      [7] B. J., Heckerman D., and Kadie C., Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, page 4352, 1998.

      [8] B.N. Miller, J.A. Konstan, J. Riedl, PocketLens: Toward a personal recommender system, ACM Transactions on Information Systems (TOIS), 22 (2004) 437-476. [8] B.N. Miller, J.A. Konstan, J. Riedl, PocketLens: Toward a personal recommender system, ACM Transactions on Information Systems (TOIS), 22 (2004) 437-476.

      [9] C. Huang and J. Yin “Effective Association Clusters Filtering to Cold-Start

      [10] Dr. B. Nayak, C A. V. Batth, “Association Rules Mining using Apriori algorithm for work-related beliefs of Generation X and Generation Y, 2015, 3324-3330.

      [11] Y. Jiang, J. Liu, M. Tang and X. (Frank) Liu “An Effective Web Service Recommendation Method based on Personalized Collaborative Filteringâ€, 2011 IEEE International Conference on Web Services.

      [12] M. O Mohany, n. Hurley, n. Kushmerick and G. Silverstre, “ Collaborative Recommendation: A Robustness Analysisâ€, ACM Transacrions on Internet Technology Vol. 4, November 2004, 344-377.

      [13] X N Lam, T Vu, T. Duc Le, A. Duc Duong, “Addressing Cold-Start in Recommendation Systemsâ€, proceedings of the 2nd international conference on Ubiquitous information management and communication,2008,208-211

      [14] I. Schein, A. Popescul, L. H. Ungar and D. M. Pennock, “ Methods and Metrics for Cold-Start Recommendationsâ€, ACM1-58113—561-0/02/0008,2002.

      [15] Hao Ma,â€An Experimantal Study on Implicit Social Recommendationâ€,2013, ACM 978-1-4503-2034-4/13/07.

      B. M. Sarwar, G. Karypis, J. A. Konstan and J. T. Reidl, “ Application of Dimensionality Reduction in Recommender System – A Case Studyâ€, Defence Technical Information Center, ADA439541

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  • How to Cite

    Kumar Ojha, R., & Bhagirathi Nayak, D. (2018). Application of Machine Learning in Collaborative Filtering Recommender Systems. International Journal of Engineering & Technology, 7(4.38), 213-215. https://doi.org/10.14419/ijet.v7i4.38.24445