Beyond the Hype : Building a Personalized Movie Experience with Content-Based Recommendation

Debangan Mandal; Mohammad Aasim; Piyush Drig; Rajarshi Biswas; Rutvik Ramteke; Manpreet Kaur

doi:10.32628/CSEIT24102129

Authors

Debangan Mandal Computer Science and Engineering, Lovely Professional University, Phagwara, India Author
Mohammad Aasim Computer Science and Engineering, Lovely Professional University, Phagwara, India Author
Piyush Drig Computer Science and Engineering, Lovely Professional University, Phagwara, India Author
Rajarshi Biswas Computer Science and Engineering, Lovely Professional University, Phagwara, India Author
Rutvik Ramteke Computer Science and Engineering, Lovely Professional University, Phagwara, India Author
Manpreet Kaur Computer Science and Engineering, Lovely Professional University, Phagwara, India Author

DOI:

https://doi.org/10.32628/CSEIT24102129

Keywords:

Movie, Recommendation System, Content-Based, Count Vectorizer, Porter Stemmer, Cosine Similarity, NLP

Abstract

This review paper provides a detailed analysis of how a movie recommendation system based on content was planned, executed and evaluated using Streamlit framework. The exponential expansion of digital content has led to the need for efficient recommendation systems, particularly in the realm of movies. In this respect, the proposed recommender system utilizes cosine similarity computations and content-based filtering methods to offer personalized film suggestions relying upon various features such as genres, keywords, castings as well as crews. The author further illustrates rigorous data preparation methods which encompass feature engineering, data collection and cleaning techniques. Furthermore, various other data sources such as TMDb API have been integrated to obtain detailed information about movies. The system, after a thorough study, shows that it has the ability of offering personalized movie recommendations that improve user engagement and experience. This paper examines broader implications of recommendation algorithms and argues their importance for improving happiness and content discovery in digital entertainment platforms. It also raises some common issues encountered during system development and advocates further research work aimed at enhancing recommendation algorithms and making them more flexible and scalable. In this way, it advances our understanding on how personalized content delivery can be applied to shape the landscape of digital entertainment through unmasking the complexities of recommendation systems.

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