Personalized Movie Recommendations with Neo4
Personalized Movie Recommendations with Neo4j is a project designed to create a dynamic movie recommendation system. Leveraging the power of Neo4j's graph database, it combines graph-based data modeling with advanced filtering techniques to deliver real-time, personalized movie suggestions. By integrating content-based filtering, collaborative filtering, and even visual data analysis through image recognition, this system pushes the boundaries of traditional recommendation methods to provide users with highly relevant and tailored suggestions.
NEO4JGRAPH-DATABASECYPHER-QUERYGOOGLE VISIONNLPPYTHON-SCRIPTING
1 min read
Main Features
Real-Time Recommendations: Neo4j's graph model ensures recommendations are both immediate and constantly updated.
Graph-Based Analysis: Incorporates relationships between movies, users, genres, keywords, and more for in-depth data synthesis.
Personalized Suggestions: Tailors recommendations to user preferences and historical interactions.
Keyword Extraction: Uses NLP techniques to analyze movie plots, enhancing recommendations with thematic connections.
Visual Content Recognition: Employs Google Vision API to analyze movie posters, enriching recommendations with visual metadata.
Technology Stack
Database: Neo4j for graph data modeling and query execution.
Programming Language: Python for data preprocessing and integration tasks.
Natural Language Processing: NLTK library for keyword extraction from movie descriptions.
Image Recognition: Google Vision API for extracting descriptive labels from movie posters.
Recommendation Algorithms: Implementations of content-based filtering, collaborative filtering, and similarity metrics (Jaccard, Cosine, Pearson).
