Document Type : Research Paper
Authors
Department of Master of Application, Jain University, Knowledge Campus, Bengalore, Karnataka, India.
Abstract
Nowadays, the recommendation system has made finding the things easy that we need. Movie recommendation systems aim at helping movie enthusiasts by suggesting what movie to watch without having to go through the long process of choosing from a large set of movies which go up to thousands and millions that is time consuming and confusing. In this article, our aim is to reduce the human effort by suggesting movies based on the user’s interests. To handle such problems, we introduced a model combining both content-based and collaborative approach. It will give progressively explicit outcomes compared to different systems that are based on content-based approach. Content-based recommendation systems are constrained to people, these systems don’t prescribe things out of the box, thus limiting your choice to explore more. Hence, we have focused on a system that resolves these issues.
Keywords
Main Subjects
[1] Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research, 8(4), 975-982.
[2] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255-263.
[3] Smarandache, F., & Broumi, S. (Eds.). (2019). Neutrosophic graph theory and algorithms. Engineering Science Reference.
[4] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255-263.
[5] Mohapatra, H. (2009). HCR using neural network (Doctoral dissertation, Biju Patnaik University of Technology). Retrieved from
https://www.academia.edu/39142624/HCR_USING_NEURAL_NETWORK
https://www.academia.edu/39142624/HCR_USING_NEURAL_NETWORK
[6] Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT. IET wireless sensor systems, 9(6), 447-457.
[7] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET wireless sensor systems, 9(6), 358-365.
[8] Mohapatra, H., Debnath, S., & Rath, A. K. (2019). Energy management in wireless sensor network through EB-LEACH (No. 1192). Easy Chair.
[9] Nirgude, V., Mahapatra, H., & Shivarkar, S. (2017). Face recognition system using principal component analysis & linear discriminant analysis method simultaneously with 3d morphable model and neural network BPNN method. Global journal of advanced engineering technologies and sciences, 4(1), 1-6.
[10] Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in Heterogeneous environment. International journal of scientific & technology research, 8(8), 1009-1013.
[11] Mohapatra, H., Rath, A. K. (2020). Fault-tolerant mechanism for wireless sensor network. IET Wireless sensor systems, 10(1), 23-30.
[12] Swain, D., Ramkrishna, G., Mahapatra, H., Patr, P., & Dhandrao, P. M. (2013). A novel sorting technique to sort elements in ascending order. International journal of engineering and advanced technology, 3(1), 212-126.
[13] Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and computer-integrated manufacturing, 28(1), 75-86.
[14] Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
[15] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple T-Norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.
[16] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic subgroup. In Neutrosophic graph theory and algorithms (pp. 213-259). IGI Global.
[17] Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology.
[18] Zheng, H., Liu, D., Wang, J., & Liang, J. (2019). A QoE-perceived screen updates transmission scheme in desktop virtualization environment. Multimedia tools and applications, 78(12), 16755-16781.
[19] Broumi, S., Dey, A., Talea, M., Bakali, A., Smarandache, F., Nagarajan, D., & Kumar, R. (2019). Shortest path problem using Bellman algorithm under neutrosophic environment. Complex & intelligent systems, 5(4), 409-416.
[20] Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems, 24, 134-149.
[21] Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global.
[22] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. Infinite Study.
[23] Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering, 8, 724-731.
[24] Kumar, R., Edaltpanah, S. A., Jha, S., & Broumi, S. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems, 23(1), 2.
[25] Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA), 9(2), 132-161.
[26] Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering, 4(1), 1-7.
[27] Chiru, C. G., Preda, C., Dinu, V. N., & Macri, M. (2015, September). Movie recommender system using the user's psychological profile. 2015 IEEE international conference on intelligent computer communication and processing (ICCP) (pp. 93-99). IEEE.
[28] Hande, R., Gutti, A., Shah, K., Gandhi, J., & Kamtikar, V. (2016). MOVIEMENDER-A movie recommender system. International journal of engineering sciences & research technology (IJESRT), 5(11), 686.
[29] Kumar, M., Yadav, D. K., Singh, A., & Gupta, V. K. (2015). A movie recommender system: Movrec. International journal of computer applications, 124(3), 7-11.
[30] Virk, H. K., Singh, E. M., & Singh, A. (2015). Analysis and design of hybrid online movie recommender system. International journal of innovations in engineering and technology (IJIET), 5(2),159-163.
[31] Mirizzi, R., Di Noia, T., Ragone, A., Ostuni, V. C., & Di Sciascio, E. (2012, January). Movie Recommendation with DBpedia. Proceedings of the third Italian information retrieval workshop, IIR (pp. 101-112). https://dblp.org/
[32] Cui, B. B. (2017). Design and implementation of movie recommendation system based on Knn collaborative filtering algorithm. ITM web of conferences (Vol. 12, p. 04008). EDP Sciences.
[33] Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k means clustering algorithm for prediction of students’ academic performance. International journal of computer science and information security, 7(1), 292-295.
[34] De Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A. (2010). Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. International journal of approximate reasoning, 51(7), 785-799.
[35] Kuzelewska, U. (2014). Clustering algorithms in hybrid recommender system on movielens data. Studies in logic, grammar and rhetoric, 37(1), 125-139.
[36] Sharma, M., & Mann, S. (2013). A survey of recommender systems: approaches and limitations. International journal of innovations in engineering and technology, 2(2), 8-14.
[37] Li, P., & Yamada, S. (2004, December). A movie recommender system based on inductive learning. IEEE conference on cybernetics and intelligent systems (pp. 318-323). Singapore, Singapore: IEEE.
[38] Scharf, P. C., & Alley, M. M. (1993). Spring nitrogen on winter wheat: II. A flexible multicomponent rate recommendation system. Agronomy journal, 85(6), 1186-1192.
[39] Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Paper presented at the AAAI/IAAI, Menlo Park. Retrieved from https://www.aaai.org/Papers/Workshops/1998/WS-98-08/WS98-08-002.pdf
[40] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web (pp. 285-295). Hong Kong: Association for Computing Machinery.
[41] Bomhardt, C. (2004, September). Newsrec, a svm-driven personal recommendation system for news websites. IEEE/WIC/ACM international conference on web intelligence (WI'04) (pp. 545-548). IEEE.
[42] Manikrao, U. S., & Prabhakar, T. V. (2005, August). Dynamic selection of web services with recommendation system. International conference on next generation web services practices (NWESP'05). DOI: 10.1109/NWESP.2005.32
[43] Von Reischach, F., Guinard, D., Michahelles, F., & Fleisch, E. (2009, March). A mobile product recommendation system interacting with tagged products. 2009 IEEE international conference on pervasive computing and communications (pp. 1-6). IEEE.
[44] Choi, K., Yoo, D., Kim, G., & Suh, Y. (2012). A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electronic commerce research and applications, 11(4), 309-317.
[45] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.
[46] Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM transactions on information systems (TOIS), 22(1), 5-53.
[47] Miyahara, K., & Pazzani, M. J. (2000, August). Collaborative filtering with the simple Bayesian classifier. Pacific rim international conference on artificial intelligence (pp. 679-689). Berlin, Heidelberg: Springer.
[48] Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM transactions on information systems (TOIS), 22(1), 89-115.
[49] Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2008, March). Image restoration by sparse 3D transform-domain collaborative filtering. Image processing: algorithms and systems VI (Vol. 6812, p. 681207). https://www.spiedigitallibrary.org
[50] Pennock, D. M., Horvitz, E. J., Lawrence, S., & Giles, C. L. (2013). Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. Proceedings of the sixteenth conference on uncertainty in artificial intelligence (UAI2000). https://arxiv.org
[51] Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-based systems, 56, 156-166.
[52] Dakhel, G. M., & Mahdavi, M. (2011, December). A new collaborative filtering algorithm using K-means clustering and neighbors' voting. 2011 11th International conference on hybrid intelligent systems (HIS) (pp. 179-184). IEEE.
[53] Chen, H. W., Wu, Y. L., Hor, M. K., & Tang, C. Y. (2017, July). Fully content-based movie recommender system with feature extraction using neural network. 2017 International conference on machine learning and cybernetics (ICMLC) (Vol. 2, pp. 504-509). IEEE.
[54] Çano, E., & Morisio, M. (2017). Hybrid recommender systems: a systematic literature review. Intelligent data analysis, 21(6), 1487-1524.
[55] Bell, R. M., & Koren, Y. (2007, October). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Seventh IEEE international conference on data mining (ICDM 2007) (pp. 43-52). IEEE.
[56] Manikandan, S., Caroline, A. L., Kanniamma, D. (2018). The study on clustering analysis on data mining. International journal of data mining techniques and applications, 7(1), 46-49.
[57] Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O'Callaghan, L. (2003). Clustering data streams: Theory and practice. IEEE transactions on knowledge and data engineering, 15(3), 515-528.
[58] Deshpande, M., & Karypis, G. (2004). Item-based top-n recommendation algorithms. ACM transactions on information systems (TOIS), 22(1), 143-177.
[59] Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
[60] Ansari, A., Essegaier, S., Kohli, R. (2000). Internet recommendation systems. Journal of marketing research, 37, 363-375.
[61] Arora, G., Kumar, A., Devre, G. S., & Ghumare, A. (2014). Movie recommendation system based on users’ similarity. International journal of computer science and mobile computing, 3(4), 765-770.
[62] Bilge, A., Kaleli, C., Yakut, I., Gunes, I., & Polat, H. (2013). A survey of privacy-preserving collaborative filtering schemes. International journal of software engineering and knowledge engineering, 23(08), 1085-1108.
[63] Gupta, U., & Patil, N. (2015, June). Recommender system based on hierarchical clustering algorithm chameleon. 2015 IEEE International advance computing conference (IACC) (pp. 1006-1010). IEEE.
[64] Bhatt, B., Patel, P. P. J., Gaudani, P. H. (2014). A review paper on machine learning based recommendation system. International journal of engineering development and research, 2(4), 3955-3961.
[65] Yih, W. T. (2009, August). Learning term-weighting functions for similarity measures. Proceedings of the 2009 conference on empirical methods in natural language processing: Volume 2-Volume 2 (pp. 793-802). Association for computational linguistics.
[66] Zhao, G., Qian, X., & Xie, X. (2016). User-service rating prediction by exploring social users' rating behaviors. IEEE transactions on multimedia, 18(3), 496-506.
[67] Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence. https://doi.org/10.1155/2009/421425
[68] Campadelli, P., Casiraghi, E., & Ceruti, C. (2015, September). Neighborhood selection for dimensionality reduction. International conference on image analysis and processing (pp. 183-191). Cham: Springer.
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