Abstract
Nowadays, the web series has turned out to be part and parcel of viewers entertainment world. At present when people can’t find out so much time from their busy schedule to take a correct decision over enormous options and happiness depends on others suggestions, they prefer to select or purchase a new product based on the feed-back from other users who have used a similar or similar type of products. Instead of doing these time-consuming tasks manually, a recommender system needs to be implemented to do all these tasks automatically. In this project, a Machine learning-based web Series recommendation system has been designed to suggest web series to the users which they have not watched yet. In this recommendation system at first, some parameters have been taken as features of tagging, and all the elements have been mapped with their respective id created separately for each of the selected para-meters and using tagging, we have got different classes for a list of web series. After tagging all the features of web series have been mapped and a recommendation system has been used to recommend unseen but preferable movies to the users. Different types of analysis on parameters which are used in our system and their results and different graphs prove the robustness of our recommendation system.
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References
Roy, D., Kundu, A.: Design of movie recommendation system by means of collaborative filtering. Int. J. Emerg. Technol. Adv. Eng. 3, 67–72 (2013)
Ho, A.T., Menezes, I.L.L., Tagmouti, Y.: E-MRS: emotion based movie recom-mender system. In: Proceedings of IADIS E-commerce Conference. University of Washington Both-ell, USA, pp. 1–8 (2006)
Pang, B., Lee, L., Vaithyanathan, S.: sentiment classification using machine learning techniques. Comput. Intell. 22, 110–125 (2006)
ENCOG: Encog framework for training data-set (2016)
Arora, G., Kumar, A., Devre, G.S., Ghumare, A.: Movie recommendation system based on user’s similarity. IJCSMC J. 3, 765–770 (2014)
Wakil, K., Ali, K., Bakhtyar, R., Alaadin, K.: Improving web movie recommender system based on emotions. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 6, 218–226 (2015)
Kumar, M., Yadav, D.K., Singh A., Gupta, V.K.: A movie recommender system: MOVREC. In: International Journal Of Computer Applications, ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 124, pp. 0975–8887 (2015). Intelligent Movie Recommender System Using Machine Learning 109
Amini, M., Nasiri, M., Afzali, M.: Sentiment classification using machine learning techniques. (IJCSIS) Int. J. Comput. Sci. Inf. Secur. 12 (2014)
Science Direct. Definition of cold start problem (2016). http://www.sciencedirect.com/science/article/pii/S0306437914001525
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of theCOLING conference (2004). http://www.isi.edu/natural-language/people/hovy/papers/04Coling-opinionvalences.pdf
Deloitte US. Deloitte Digital Democracy Survey, 11th edition. URL: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technology-media-telecommunications/us-tmt-deloitte-dig-ital-democracy-executive-summary.pdf
Deloitte US, Deloitte Digital Democracy Survey, 9th edition. URL: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Technology-Media-Telecommunications/gx-tmt-deloitte-de-mocracy-survey.pdf
Mahata, A., Saini, N., Saharawat, S., Tiwari, R.: Intelligent movie recommender system using machine learning. In: Basu, A., Das, S., Horain, P., Bhattacharya, S. (eds.) IHCI 2016. LNCS, vol. 10127, pp. 94–110. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52503-7_8
Postmus, S.: Recommender system techniques applied to Netflix movie data. (s.h.postmus@student.vu.nl)
Fernández, L.E.M.: Recommendation System for Netflix
Kumar, L., Mitra, A., Mittal, M., Sanghvi, V., Roy, S., Setua, S.K.: Music tagging and similarity analysis for recommendation system. In: Das, A.K., Nayak, J., Naik, B., Pati, S.K., Pelusi, D. (eds.) Computational Intelligence in Pattern Recognition. AISC, vol. 999, pp. 477–485. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9042-5_40
Bayle, Y., Robine, M., Hanna, P.: SATIN: a persistent musical database for music information retrieval and a supporting deep learning experiment on song instrumental classification. Multimedia Tools Appl. 78(3), 2703–2718 (2018). https://doi.org/10.1007/s11042-018-5797-8
Das, N., Borra, S., Dey, N., Borah, S.: Social networking in web based movie recommendation system. In: Dey, N., Babo, R., Ashour, A.S., Bhatnagar, V., Bouhlel, M.S. (eds.) Social Networks Science: Design, Implementation, Security, and Challenges, pp. 25–45. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90059-9_2
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Mitra, A. et al. (2022). Web Series Recommendation System Using Machine Learning. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_31
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DOI: https://doi.org/10.1007/978-3-030-89701-7_31
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