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People are puzzled about which movie to watch these days because there are so many movies available on various OTT platforms. A recommender system would solve this problem by recommending the best movie to the user based on his genre, actor, director, and rating preferences. The cosine similarity principle would be used to guide the recommendation system. Apart from that, we will use the Tfidftransformer and count vectorizer from the sci-kit-learn library in Python in this work. In this study work, all of the approaches' constraints have been described. All of this work was done using datasets from several OTT platforms that were available on Kaggle.
G. Adomavicius, and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-artand possible extensions”, Knowledge and Data Engineering, IEEE Transactions, 17(6):734–749, 2005.
R. B. Yates, B. R.-Neto, et al. ‘Modern information retrieval”, volume 463. ACM Press New York, 1999.
S. Baluja, R.Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly, “Video suggestion and discovery for youtube: taking random walks through the view graph”, In Proceedings of the 17th international conference on World Wide Web, pages 895–904. ACM, 2008.
X. Hailing, W. Xiao, L. Xiaodong, and Y. Baoping, “Comparison study of Internet recommendation system”, Journal of Software, 20(2):350–362, 2009.
T. E. D. Mining, “Enhancing teaching and learning through educational data mining and learning analytics: An issue brief,” in Proceedings of a conference on advanced technology for education, 2012.
Nakagawa and T. Ito, “An implementation of a knowledge recommendation system based on similarity among users profiles,” in Proceedings of the Sice Conference, pp. 326–327 vol.1, 2002.
T. K. Quan, I. Fuyuki, and H. Shinichi, “Improving the accuracy of recommender system by clustering items based on the stability of user similarity,” in International Conference on Computational Intelligence for Modelling Control and Automation, 2006.
M. Muozorganero, G. A. Ramezgonzlez, P. J. Muozmerino, andC. D Kloos, “A collaborative recommender system based on space-time similarities,” vol. 9, no. 3, pp. 81–87, 2010.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web. ACM, 2001, pp. 285–295.
G. Wang, “Survey of personalized recommendation system,” Computer Engineering & Applications, 2012.
Albadvi and M. Shahbazi, “A hybrid recommendation technique based on product category attributes,” Expert Systems with Applications, vol. 36, no. 9, pp. 11 480–11 488, 2009.
F. R. Hernandez and N. Y. G. Garcia, “Distributed processing using cosine similarity for mapping big data in Hadoop,” IEEE Latin America Transactions, vol. 14, no. 6, pp. 2857–2861, 2016.
A. Gupta, and Dr. B. K. Tripathy, “A generic hybrid recommender system based on neural networks”, Advance Computing Conference (IACC), 21-22 February 2014.
G. L. Giller, ”The Statistical Properties of RandomBit-streams and the Sampling Distribution of Cosine Similarity”, Giller Investments Research Notes, 2012.
N. Rengaraj, C. M. Kavitha, S. Loganathan, and N.Muthurasu, “A study of existing systems of recommendation engines”, National Conference of Emerging ComputingTechnologies and Applications, Vel Tech University, Avadi, Chennai. 05-06 April 2018.
R. Bell, Y. Koren, and C. Volinsky, “Modelling relationships at multiple scales to improve the accuracy of large recommender systems”, In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 95–104. ACM, 2007.
S. Bhargav, “Efficient features for movie recommendation systems”, 2014.
D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, “Recommender systems: an introduction”, Cambridge University Press, 2010.
J. A Konstan, “Introduction to recommender systems: Algorithms and evaluation”, ACM Transactions on InformationSystems (TOIS), 22(1):1–4, 2004.
P. Vilakone, D. Park, K. Xinchang, et al. “An Efficient movie recommendation algorithm based on improved k-clique”, Hum.Cent. Comput. Inf. Sci. 8, 38 (2018).