Matrix Factorization Model with Audio Data for Music Recommendation

Matrix Factorization is a popular collaborative filtering technique used for recommendation systems. In the context of music recommendation, this model aims to predict user preferences by factoring the user-item interactions matrix. It decomposes the original matrix into two lower-dimensional matrices, one representing users and the other representing items, where each user and item is represented by a latent feature vector. By performing matrix factorization, the missing entries in the matrix can be predicted, enabling personalized music recommendations for users.

Pros:

• Can capture latent factors and provide personalized recommendations.
• Works well with sparse data, typically present in music recommendation systems.
• Can handle incremental updates and real-time recommendations.
• Provides transparency and interpretability in terms of user-item relationships.

Cons:

• Cold start problem: It struggles to make accurate recommendations for new users or items with limited data.
• Difficulty in dealing with large-scale datasets due to higher computational requirements.
• Vulnerable to popular item bias, where popular items get recommended more often.
• Difficulty in incorporating contextual information, such as time, location, or mood.

1. Personalized Music Streaming Platforms: Matrix Factorization can be used to recommend music to individual users based on their listening history and preferences, thus enhancing user satisfaction and engagement.
2. Music Recommendation for Online Marketplaces: E-commerce platforms selling music-related products can leverage Matrix Factorization to suggest relevant music items to users based on their previous purchases and preferences.
3. Collaborative Playlist Generation: Matrix Factorization can assist in generating collaborative playlists by suggesting songs to users based on their common interests and musical preferences.

• Introduction to Recommender System and Matrix Factorization
• Matrix Factorization for Recommender Systems
• Deep Learning for Recommender Systems

1. Junyuan Hong
2. Yehuda Koren
3. Chris Deotte
4. Hugues Demers
5. Alexis Jacq

*Note: The expertise of these individuals is not guaranteed and may vary over time.