SONG RECOMMENDATION
SYSTEM: DESIGNING A
RECOMMENDATION SYSTEM
By: Drisya syam kumar
Anshy prabha
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Designing a Song Recommendation System with AI, Study Guides, Projects, Research of Artificial Intelligence
An overview of designing a song recommendation system, focusing on personalized music discovery based on user preferences. It explores various recommendation techniques, including collaborative filtering and content-based filtering, to address the limitations of current systems. The document highlights the use of machine learning algorithms, real-time recommendations, and user feedback to enhance personalization. It also includes a case study of spotify's ai-powered music recommendation system, which employs collaborative filtering, content-based filtering, and natural language processing to create custom playlists. The document emphasizes the importance of user feedback, machine learning, and evaluation metrics in creating effective music recommendation systems, ultimately aiming to deliver contextually relevant and highly personalized recommendations.
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2024/2025
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SONG RECOMMENDATION
SYSTEM: DESIGNING A
RECOMMENDATION SYSTEM
By: Drisya syam kumar
Anshy prabha
INTRODUCTION What is a recommendation system?
- A recommendation system is an algorithm used to predict the most relevant items for a user based on various factors like preferences, behaviors, and data patterns.
- Commonly used in e-commerce, social media, and streaming platforms.
THE PROBLEM The current music recommendation system are not personalized and often fails to provide relevant recommendations. The project aim to solve this problem by creating a system that is based on user’s preference and behavior.
- (^) Content based filtering : This method recommends music based on the similarity of its attributes (e.g., genre, tempo, key) to music that the user has previously enjoyed.
- (^) Collaborative Filtering: uses the preferences of similar users to recommend music. If a user who likes similar music to a user has also enjoyed a particular song, the system might recommend that song to the user. SOLUTION
- (^) Machine Learning: Machine learning algorithms can be trained on user data and music features to predict which songs a user is likely to enjoy.
- (^) Real-time Recommendations: A good recommendation system should be able to provide recommendations in real-time, allowing users to discover new music quickly.
USER FEEDBACK Feedback from users is essential for developing and perfecting music recommendation systems due to adaptive learning and personalization opportunities. By allowing user to rate and provide feedback on recommended music, we can refine our algorithm and create more personalized experience. This feedback can also be used to improve the overall user experience of the system.
CASE STUDY Spotify’s AI-Powered Music Recommendation System Spotify relies on a sophisticated AI recommendation system which employs collaborative filtering, content-based filtering and natural language processing. It analyzes user actions like playing, skipping, and liking songs along with sonic attributes and web text data to create custom playlists such as Discover Weekly and Daily Mixes. Deep learning models, including CNNs, analyze unprocessed audio files to extract features of the songs. This mixed strategy is crucial for Spotify to enhance engagement, capture more users, and provide a customized listening experience that adapts to user input.
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