Keywords - Content filtering, Collaborative filtering, NLP, Matrix Factorization, Singular Value Decomposition (SVD), Tfidf Vectorization, Cosine similarity, Cross-Validation, Precision-at-k, Recall-at-k, Sklearn, Python, Pandas, Dask, Matplotlib
Music recommendation systems are algorithms designed to suggest music to users based on their past and current preferences, listening history, and behavior of other similar users on the same platform.This needs sophistication in providing personalized experience and enhancing engagement on the platform, leading to better customer retention and satisfaction.
An ideal system should be able to handle -
Cold Start Problem - new items or users with limited interaction history
Filter Bubble - reinforcing existing user preferences, lacking diversity
Dynamic Preference - adapting quickly to user's evolving taste over time
Popularity Bias - popular items get more popular in positive feedback loop
Business Questions
Sophisticated music recommendation systems are required to address various business, strategic and operational questions. Some of them are -
User Retention and Engagement - How can we improve user retention and keep users engaged on our platform ?
Content Discovery and Diversity - How can we help users discover new and diverse music content ?
Monetization strategies - What strategies can we deploy in our recommendation system to optimize revenue generation ?
Targeted marketing - How can we segment our user base for effective marketing ?
Competitive edge - How can we maintain/improvise our recommendation system to have a competitive edge in the industry ?
Adaptive user interface - How can we create adaptive user interfaces to enhance usability ?
In this notebook, we will test two approaches -
Collaborative Filtering - recommending items based on other similar users behavior.
Content Filtering - recommending items based on similarity of tracks to the user's listening history.
Comments