Adaptive AI Playlist Generator

Adaptive music recommendation system that learns listener preferences over time and generates explainable playlists using embeddings and contextual signals.

PythonMachine LearningRecommendation SystemsEmbeddingsAPI IntegrationSystem Design

This project is private because it is in active product development.

Modeling

taste embeddings + memory

Output

explainable playlist ranking

Project Overview

Designed a recommendation architecture that models changing music taste over time, explains suggestions, and balances novelty with preference continuity.

Challenge

Static playlist rules fail to capture evolving user taste and context, which leads to repetitive or low-relevance recommendations.

Solution

Built an adaptive recommendation system with preference embeddings, taste-decay logic, and contextual weighting to generate personalized playlists.

Results

Delivered a functional explainable playlist pipeline that demonstrates practical recommendation-system design and product-oriented ML engineering.