ARCHIVES

[1] Lakshmi Tharun Ponnam, Sreenivasa Deepak, Siva Nagaraju and Srikanth Yellamati “Movie Recommender System Using Item Based
Collaborative Filtering Technique” IEEE 2016 International conference on Emerging trends in Engineering , Technology and Science.
[2] Mukesh Kumar Karita, Atul Kumar and Pardeep Singh “Item-Based Collaborative Filtering in Movie Recommendation in Real time”
IEEE 2018 First International Conference on Secure Cyber computing and Communication, 2018.
[3] Rui-sheng Zhang, Qi-dong Liu, Chun-Gui, Jia-Xuan Wei, Huiyi-Ma “Collaborative Filtering for Recommender Systems” IEEE 2014
Second International Conference on Advanced Cloud And Big Data.
[4] Resnick P, Iacovou N, Suchak M, et al. GroupLens: an open architecture for collaborative filtering of netnews[C]//Proceedings of the
1994 ACM conference on Computer supported cooperative work. ACM, 1994: 175-186
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14th Conf. on Uncertainty in Artificial Intelligence, pages 43–52, Madison, Wisconsin, USA, 1998.
[6] Ma H, King I, Lyu M R. Effective missing data prediction for collaborative filtering[C]//Proceedings of the 30th annual international
ACM SIGIR conference on Research and development in information retrieval. ACM, 2007: 39-46.
[7] Gori M, Pucci A. ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines[C]//IJCAI. 2007, 7: 2766-2771.
[8] Fouss F, Pirotte A, Renders J M, et al. Random-walk computation of similarities between nodes of a graph with application to
collaborative recommendation[J]. Knowledge and Data Engineering, IEEE Transactions on, 2007, 19(3): 355-369.
[9] Luo H, Niu C, Shen R, et al. A collaborative filtering framework based on both local user similarity and global user similarity [J].
Machine Learning, 2008, 72(3): 231-245.
[10] M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Application of dimensionality reduction in recommender system—a case study.
In Proc. of WebKDD-00, Web Mining for E-Commerce Workshop, at 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data
Mining, Boston, Massachusetts, USA, 2000.
[11] Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1):89–115, 2004.
[12] Salakhutdinov and A.Mnih. Probabilistic matrix factorization. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in
Neural Information Processing Systems 20.MIT Press, Cambridge, Massachusetts, USA, 2008.
[13] Paterek, Arkadiusz. "Improving regularized singular value decomposition for collaborative filtering." Proceedings of KDD cup and
workshop. Vol. 2007. 2007.
[14] Takacs, Gabor, et al. "On the gravity recommendation system." Proceedings of KDD cup and workshop. Vol. 2007. 2007.
[15] Yuan-hong, Wu, and Tan Xiao-qiu. "A real-time recommender system based on hybrid collaborative filtering." Computer Science and Education (ICCSE), 2010 5th International Conference on. IEEE, 2010.
[16] Xu, Hai-Ling, et al. "Comparison study of Internet recommendation system." Journal of software 20.2 (2009): 350-362.
[17] Bell, Robert M., Yehuda Koren, and Chris Volinsky. "The BellKor solution to the Netflix prize." KorBell Team’s Report to Netflix
(2007).
[18] Burke, Robin. "Hybrid recommender systems: Survey and experiments." User modeling and user-adapted interaction 12.4 (2002):
331-370
[19] P. Resnick and H. R. Varian: “Recommender Systems”, Communications of the ACM, vo1.40, pp.56-58, 1997
[20] Hill, W.C., Stead, L., Rosenstein, M. and Furnas, G. “Recommending and Evaluating Choices in a Virtual Community of Use”, in
Proceedings of CHI’95 (Denver CO, May 1995), ACM Press, 194-201