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A Unified Approach to Movie and Music Recommendations with Hybrid ML

1P. BINDHU PRIYA,2 MOGALATHURTHI BHARATH RAJU

1Assistant Professor, Department Of MCA, 2MCA Final Semester,

1Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

Abstract:

In the era of exponential digital content growth, users are often overwhelmed by the vast range of available options, leading to decision fatigue and decreased satisfaction. Effective recommendation systems have therefore become a cornerstone for enhancing user engagement, retention, and overall platform value. This project presents a unified hybrid recommendation system designed to deliver highly relevant movie and music suggestions tailored to individual user preferences. The proposed system integrates content-based filtering, collaborative filtering, and feature similarity analysis to maximize recommendation accuracy. For movies, the content-based component employs TF-IDF (Term Frequency–Inverse Document Frequency) vectorization of movie overviews to capture semantic similarities, enabling recommendations of movies with closely related themes, plots, and genres. Complementing this, a collaborative filtering approach using Truncated Singular Value Decomposition (SVD) predicts personalized ratings by analyzing patterns in historical user–item interactions, thus capturing latent preference factors. For the music recommendation module, the system processes structured audio feature datasets containing parameters such as danceability, energy, loudness, tempo, and valence. By applying cosine similarity to these numerical feature vectors, it can recommend either artists or genres that closely match the acoustic profile of a selected choice. This dual-level recommendation—artist-based and genre-based—allows for flexible exploration and discovery of new music aligned with a listener’s tastes. The entire solution is implemented using Python and Streamlit, offering an intuitive and interactive web-based interface. Users can seamlessly switch between movie and music recommendations, select the type of filtering, and instantly receive results in a visually organized format. The hybrid methodology ensures the system is capable of handling both textual (movie metadata) and numerical (music audio features) data, making it a versatile framework adaptable to other multimedia domains such as books, podcasts, or videos. Overall, this project demonstrates how combining multiple recommendation strategies within a unified architecture can lead to a richer, more personalized user experience, improving both content discoverability and user satisfaction while maintaining scalability and adaptability for future enhancements.

Keywords: Hybrid Recommendation System, Content-Based Filtering, Collaborative Filtering, TF-IDF, Truncated Singular Value Decomposition (SVD), Cosine Similarity, Movie Recommendation, Music Recommendation, Audio Feature Analysis, Streamlit, Personalized Content Discovery, Multimedia Recommendation, User Preference Modelling, Feature Similarity Analysis, Digital Content Overload.