Graph-based music recommendation and analytics system built with Neo4j and Cypher to explore relationships between artists, genres, users, and listening patterns.
This project explores how graph databases can be applied to music recommendation systems and connected data analysis.
Using Neo4j, the application models musical relationships as graph structures, enabling the exploration of:
- Artist connections
- Genre relationships
- User preferences
- Listening behavior
- Recommendation patterns
The project demonstrates how graph traversal and relationship analysis can improve recommendation and discovery systems.
- Graph-based music recommendation
- Artist and genre relationship modeling
- Listening pattern analysis
- Cypher queries for graph exploration
- Recommendation traversal techniques
- Connected music data analytics
- Neo4j
- Cypher
- Graph Databases
- Recommendation Systems
- Data Analytics
The application uses Neo4j to represent music-related entities and relationships through graph structures.
- Users
- Artists
- Songs
- Genres
- Listening interactions
- Relationship traversal
- Similarity analysis
- Connected data exploration
- Recommendation modeling
- Graph analytics
Cypher queries are used to explore connections between musical preferences and recommendation opportunities.
The project supports analysis such as:
- Similar artist discovery
- Genre relationship exploration
- Listening behavior analysis
- User preference connections
- Recommendation path traversal
The graph structure enables efficient navigation between connected music entities and user interactions.
Through this project, I improved my knowledge in:
- Graph-based recommendation systems
- Neo4j relationship modeling
- Cypher query development
- Data analytics concepts
- Connected data structures
- Relationship traversal techniques
- Interactive analytics dashboard
- Recommendation scoring system
- Music visualization graphs
- Frontend integration
- API-based recommendation engine
Maria Eduarda Toso