Document Type : Research Paper


1 Department of MCA ,School of Computer Science &IT ,Jain (deemed-to-be) University ,Bengaluru, India.

2 Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.


Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies from the thousand already existing ones. Content-based recommendation system describes the items that may be recommended to the user. Based on a data set, it predicts what movies a user will like considering the attributes present in the previously liked movies. Recommendation systems can recommend movies based on one or a combination of two or more attributes. While designing a movie recommendation system various factors are considered such as the genre of the movie, the director or the actors present in it. In this paper, the recommendation system has been built on cast, keywords, crew, and genres. A single column is created which will be the sum of all the 4 attributes, and it acts as a dominant factor for this movie recommender system.


Main Subjects

[1]     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 and intelligent systems, 5(4), 409-416.
[2]     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.
[3]     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, 139-151.
[4]     Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. International journal of engineering and advanced technology, 8(3), 347-353.
[5]     Kumar, R., Edaltpanah, S. A., Jha, S., Broumi, S., & Dey, A. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems, 23, 5-15.
[6]     Pratihar, J., Kumar, R., Dey, A., & Broumi, S. (2020). Transportation problem in neutrosophic environment. In Neutrosophic graph theory and algorithms (pp. 180-212). IGI Global.
[7]     Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex and intelligent systems5(2), 255-263.
[8]     Pratihar, J., Kumar, R., Edalatpanah, S. A., & Dey, A. (2020). Modified Vogel’s approximation method for transportation problem under uncertain environment. Complex and intelligent systems, 1-12.
[9]     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 technology8, 385-390.
[10] 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.
[11] 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.
[12] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Soft subring theory under interval-valued neutrosophic environment. Neutrosophic sets and systems36(1), 16, 193-219.
[13] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Introduction to interval-valued neutrosophic subring. Neutrosophic sets and systems36(1), 17, 220-245.
[14]  Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic hypersoft subgroup. Neutrosophic sets and systems33(1), 208-233.
[15] Yang, Y., Yan, D., & Zhao, J. (2017). Optimal path selection approach for fuzzy reliable shortest path problem. Journal of intelligent & fuzzy systems32(1), 197-205.
[16] 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.
[17] 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 engineering4(1), 1-7.
[18] 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 engineering8(6), 724- 731.
[19] Singh, A., Kumar, A., & Appadoo, S. S. (2019). A novel method for solving the fully neutrosophic linear programming problems: Suggested modifications. Journal of intelligent & fuzzy systems37(1), 885-895.
[20] 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 research8(4), 975- 982.
[21] Mohapatra, H., Rath, S., Panda, S., & Kumar, R. (2020). Handling of man-in-the-middle attack in WSN through intrusion detection system. International journal of emerging trends in engineering research8(5), 1503-1510.
[22] Mohapatra, H., Debnath, S., & Rath, A. K. (2019). Energy management in wireless sensor network through EB-LEACH. International journal of research and analytical reviews (IJRAR), 56-61.
[23] Mohapatra, H., Rath, A. K., Landge, P. B., & Bhise, D. A. (2020). Comparative analysis of clustering protocols of wireless sensor network. International journal of mechanical and production engineering research and development (IJMPERD) ISSN (P), 10(3) 8371-8385.
[24] Mohapatra, H., & Rath, A. K. (2020). Survey on fault tolerance-based clustering evolution in WSN. IET networks, 9(4), 145-155.
[25] Mohapatra, H., Debnath, S., Rath, A. K., Landge, P. B., Gayen, S., & Kumar, R. (2020). An efficient energy saving scheme through sorting technique for wireless sensor network.International journal of emerging trends in engineering research,8(8), 4278-4286.
[26] Mohapatra, H., & Rath, A. K. (2020). Fault tolerance in wsn through uniform load distribution function. International journal of sensors, wireless communications and control10(1), 1-10.https://doi. org/10.2174/2210327910999200525164954
[27] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance through energy balanced cluster formation (EBCF) in WSN. In Smart innovations in communication and computational sciences (pp. 313-321). Springer, Singapore.
[28] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET wireless sensor systems9(6), 358-365.DOI: 10.1049/iet-wss.2018.5229
[29] Mohapatra, H (2018). C programming: practice.Amazon.
[30] Mohapatra, H., & Rath, A. K. (2020). Fundamentals of software engineering: designed to provide an insight into the software engineering concepts. BPB Publications.
[31] Mohapatra, H. I. T. E. S. H. (2009). HCR using neural network (Master's Thesis, College of Engineering and Technology, Bhubaneswar).
[32] Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in heterogeneous environment. International journal of scientific and technology research8, 1009-1013.
[33] Nirgude, V. N., Nirgude, V. N., Mahapatra, H., & Shivarkar, S. A (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, 1-6.
[34] Mohapatra, H., & Rath, A. K. (2020, October). Nub Less Sensor Based Smart Water Tap for Preventing Water Loss at Public Stand Posts. 2020 IEEE microwave theory and techniques in wireless communications (MTTW) (Vol. 1, pp. 145-150). IEEE.
[35] Mohapatra, H., & Rath, A. K. (2020). IoT-based smart water. In IOT technologies in smart-cities: from sensors to big data, security and trust. DOI: 10.1049/PBCE128E
[36] Mohapatra, H. (2020). Offline drone instrumentalized ambulance for emergency situations. International journal of robotics and automation (IJRA)9(4), 251-255.
[37] 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 systems9(6), 447-457.
[38] Panda, H., Mohapatra, H., & Rath, A. K. (2020). WSN-based water channelization: an approach of smart water. In Smart cities—opportunities and challenges (pp. 157-166). Singapore: Springer.
[39] Chen, L. S., Hsu, F. H., Chen, M. C., & Hsu, Y. C. (2008). Developing recommender systems with the consideration of product profitability for sellers. Information sciences178(4), 1032-1048.
[40] Jalali, M., Mustapha, N., Sulaiman, M. N., & Mamat, A. (2010). WebPUM: a web-based recommendation system to predict user future movements. Expert systems with applications37(9), 6201-6212.
[41] Acilar, A. M., & Arslan, A. (2009). A collaborative filtering method based on artificial immune network. Expert systems with applications36(4), 8324-8332.
[42] Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence. doi:10.1155/2009/421425
[43] Kim, H. N., El-Saddik, A., & Jo, G. S. (2011). Collaborative error-reflected models for cold-start recommender systems. Decision support systems51(3), 519-531.
[44] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering17(6), 734-749.
[45] Yu, K., Schwaighofer, A., Tresp, V., Xu, X., & Kriegel, H. P. (2004). Probabilistic memory-based collaborative filtering. IEEE transactions on knowledge and data engineering16(1), 56-69.
[46] Min, S. H., & Han, I. (2005). Detection of the customer time-variant pattern for improving recommender systems. Expert systems with applications28(2), 189-199.
[47] Celma, Ò., & Serra, X. (2008). FOAFing the music: Bridging the semantic gap in music recommendation. Journal of web semantics6(4), 250-256.
[48] Fisk, D. (1997). An application of social filtering to movie recommendation. In Software agents and soft computing towards enhancing machine intelligence (pp. 116-131). Berlin, Heidelberg: Springer.
[49] Chen, Q., & Aickelin, U. (2008). Movie recommendation systems using an artificial immune system.    6th international conference in adaptive computing in design and manufacture (ACDM 2004). arXiv preprint arXiv:0801.4287
[50] Choi, S. M., & Han, Y. S. (2010, September). A content recommendation system based on category correlations. 2010 Fifth international multi-conference on computing in the global information technology (pp. 66-70). IEEE.
[51] Son, J., & Kim, S. B. (2017). Content-based filtering for recommendation systems using multiattribute networks. Expert systems with applications89, 404-412.
[52] Basu, C., Hirsh, H., & Cohen, W. (1998, July). Recommendation as classification: Using social and content-based information in recommendation. Aaai/iaai (pp. 714-720).
[53] Debnath, S., Ganguly, N., & Mitra, P. (2008, April). Feature weighting in content based recommendation system using social network analysis. Proceedings of the 17th international conference on world wide web (pp. 1041-1042).
[54] Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
[55] Basilico, J., & Hofmann, T. (2004, July). Unifying collaborative and content-based filtering. Proceedings of the twenty-first international conference on machine learning (p. 9).
[56] Liu, J., Dolan, P., & Pedersen, E. R. (2010, February). Personalized news recommendation based on click behavior. Proceedings of the 15th international conference on intelligent user interfaces (pp. 31-40).
[57] Hameed, M. A., Al Jadaan, O., & Ramachandram, S. (2012). Collaborative filtering based recommendation system: A survey. International journal on computer science and engineering4(5), 859.
[58] Uluyagmur, M., Cataltepe, Z., & Tayfur, E. (2012, October). Content-based movie recommendation using different feature sets. Proceedings of the world congress on engineering and computer science (Vol. 1, pp. 17-24).
[59] Deldjoo, Y., Elahi, M., Cremonesi, P., Garzotto, F., Piazzolla, P., & Quadrana, M. (2016). Content-based video recommendation system based on stylistic visual features. Journal on data semantics5(2), 99-113.
[60] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM35(12), 61-70.
[61] Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., & Riedl, J. (1999). Combining collaborative filtering with personal agents for better recommendations. AAAI/IAAI439.
[62] Adomavicius, G., & Kwon, Y. (2007). New recommendation techniques for multicriteria rating systems. IEEE intelligent systems22(3), 48-55.
[63] 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 systems56, 156-166.
[64] Pan, C., & Li, W. (2010, June). Research paper recommendation with topic analysis. International conference on computer design and applications (Vol. 4, pp. V4-264). IEEE.
[65] Konstan, J. A., & Riedl, J. (2012). Recommender systems: from algorithms to user experience. User modeling and user-adapted interaction22(1-2), 101-123.