Movie Popularity Content-based Database System Using Cloud Computing Based on Deep Learning and AI

Year : 2025 | Volume : 12 | Issue : 01 | Page : 7 12
    By

    Jaishri Panchal,

  • Himanshu Solankey,

  • Akash Dighe,

  • Shweta Sathe,

  • Yash Makode,

  1. Assistant Professor, Computer Engineering, P.G. Moze College of Engineering, Wagholi, Pune, Maharashtra, India.
  2. Student, Computer Engineering, P.G. Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  3. Student, Computer Engineering, P.G. Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  4. Student, Computer Engineering, P.G. Moze College of Engineering, Wagholi, Pune, Maharashtra, India
  5. Student, Computer Engineering, P.G. Moze College of Engineering, Wagholi, Pune, Maharashtra, India

Abstract

The digital age has brought rapid growth in both the production and consumption of movies within the entertainment industry. This surge in movie availability has created a need for effective methods to assist users in discovering films that align with their preferences. This paper introduces the concept of a “Movie Popularity Content-Based Database System,” designed to enhance the movie recommendation process by combining content-based filtering with popularity trends. In summary, the “Movie Popularity Content-Based Database System” represents a pioneering approach to movie recommendations by leveraging content-based analysis, and user interactions. By delving into the inherent qualities that define movie popularity, this system offers a more comprehensive, personalized, and enriching movie-selection experience for users, catering to their unique tastes while also broadening their cinematic horizons. In the digital media age, the film industry is flourishing, offering a wide variety of movies that appeal to a broad range of tastes and preferences. Movie enthusiasts, producers, and distributors seek ways to navigate this vast landscape to identify popular and trending movies. The “Movie Popularity Content-Based Database System” is an innovative solution developed to tackle this challenge.

Keywords: Popularity trends, entertainment industry, personalized suggestions, database system, movie recommendations, film ranking, user preferences, genre analysis, viewer trends, content analysis, film database

[This article belongs to Journal of Advanced Database Management & Systems ]

How to cite this article:
Jaishri Panchal, Himanshu Solankey, Akash Dighe, Shweta Sathe, Yash Makode. Movie Popularity Content-based Database System Using Cloud Computing Based on Deep Learning and AI. Journal of Advanced Database Management & Systems. 2024; 12(01):7-12.
How to cite this URL:
Jaishri Panchal, Himanshu Solankey, Akash Dighe, Shweta Sathe, Yash Makode. Movie Popularity Content-based Database System Using Cloud Computing Based on Deep Learning and AI. Journal of Advanced Database Management & Systems. 2024; 12(01):7-12. Available from: https://journals.stmjournals.com/joadms/article=2024/view=191799


References

  1. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: WWW ’01 Proceedings of the 10th International Conference on World Wide Web, Hong Kong, May 1–5, 2001. pp. 285–295.
  2. Koren Y, Rendle S, Bell R. Advances in collaborative filtering. In: Ricci F, Rokach L, Shapira B, editors. Recommender Systems Handbook. New York, NY, USA: Springer; 2021. pp. 91–142.
  3. Sharma L, Gera A. A survey of recommendation system: research challenges. Int J Eng Trends Technol. 2013; 4 (5): 1989–1992.
  4. Das N, Borra S, Dey N, Borah S. Social networking in web based movie recommendation system. In: Dey N, Babo R, Ashour A, Bhatnagar V, Bouhlel M, editors. Social Networks Science: Design, Implementation, Security, and Challenges. Cham, Switzerland: Springer; 2018. pp. 25–45.
  5. Nagarnaik P, Thomas A. Survey on recommendation system methods. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, February 26–27, 2015. pp. 1603–1608.
  6. Hameed MA, Al Jadaan O, Ramachandram S. Collaborative filtering based recommendation system: a survey. Int J Computer Sci Eng. 2012; 4 (5): 859–876.
  7.  Kwak M, Cho DS. Collaborative filtering with automatic rating for recommendation. In: ISIE 2001, 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570), Pusan, Korea, June 12–16, 2001. Vol. 1, pp. 625–628.
  8. Ekstrand MD, Riedl JT, Konstan JA. Collaborative filtering recommender systems. Foundations Trends Human–Computer Interact. 2011; 4 (2): 81–173.
  9. Lathia N, Hailes S, Capra L. Temporal collaborative filtering with adaptive neighbourhoods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA, July 19–23, 2009. pp. 796–797.
  10. Shah L, Gaudani H, Balani P. Survey on recommendation system. Int J Computer Appl. 2016; 137 (7): 43–49.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 25/09/2024
Accepted 18/12/2024
Published 31/12/2024
155


My IP

PlumX Metrics