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

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Year : 2025 | Volume :12 | Issue : 01 | Page : –
By
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Jaishri Panchal,

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Himanshu Solankey,

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Akash Dighe,

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Shweta Sathe,

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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 document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_130445’);});Edit Abstract & Keyword

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 ‘Content-Based Movie Popularity 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 (joadms)]

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):-.
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):-. Available from: https://journals.stmjournals.com/joadms/article=2024/view=0

References
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  1. Sharma L, Gera A. A survey of recommendation system: Research challenges. International Journal of Engineering Trends and Technology (IJETT). 2013 May;4(5):1989-92.
  2. Das N, Borra S, Dey N, Borah S. Social networking in web based movie recommendation system. Social Networks Science: Design, Implementation, Security, and Challenges: From Social Networks Analysis to Social Networks Intelligence. 2018:25-45.
  3. Nagarnaik P, Thomas A. Survey on recommendation system methods. In2015 2nd international conference on electronics and communication systems (ICECS) 2015 Feb 26 (pp. 1603-1608). IEEE.
  4. Hameed MA, Al Jadaan O, Ramachandram S. Collaborative filtering based recommendation system: A survey. International Journal on Computer Science and Engineering. 2012 May 1;4(5):859.
  5. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. InProceedings of the 10th international conference on World Wide Web 2001 Apr 1 (pp. 285-295).
  6. Koren Y, Rendle S, Bell R. Advances in collaborative filtering. Recommender systems handbook. 2021 Nov 22:91-142.
  7. Kwak M, Cho DS. Collaborative filtering with automatic rating for recommendation. InISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570) 2001 Jun 12 (Vol. 1, pp. 625-628). IEEE.
  8. Ekstrand MD, Riedl JT, Konstan JA. Collaborative filtering recommender systems. Foundations and Trends® in Human–Computer Interaction. 2011 May 4;4(2):81-173.
  9. Lathia N, Hailes S, Capra L. Temporal collaborative filtering with adaptive neighbourhoods. InProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval 2009 Jul 19 (pp. 796-797).
  10. Shah L, Gaudani H, Balani P. Survey on recommendation system. International Journal of Computer Applications. 2016 Mar;137(7):43-9.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 25/09/2024
Accepted 18/12/2024
Published 31/12/2024
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