Reel Favorites
Personalized Movie Discovery Engine
A sophisticated recommendation engine that goes beyond basic genre matching to understand nuanced user preferences through collaborative filtering and natural language understanding.
Generic recommendations
miss what makes films resonate
Traditional movie recommendation systems rely heavily on genre tags and viewing history, often missing the subtle preferences that make a film truly resonate with a viewer.
Things like pacing, cinematography style, thematic depth, or emotional tone are rarely captured by conventional algorithms, leading to recommendations that feel generic and impersonal.
Hybrid recommendations that
understand nuance
Reel Favorites uses a hybrid recommendation approach combining collaborative filtering with NLP-based analysis of reviews and plot summaries. Users can describe what they're in the mood for in natural language, and the system translates that into precise recommendations.
Natural Language
Describe your mood in words, get perfect matches
Collaborative Filtering
50M+ ratings powering taste similarity
Deep Analysis
NLP parsing of reviews and themes
Intelligence behind the scenes
A multi-layered recommendation architecture that combines machine learning, graph databases, and natural language processing.
User Taste Profiling
Build a multidimensional taste profile from ratings, natural language descriptions, and browsing behavior.
Graph-Based Relationships
Neo4j maps connections between films through directors, actors, themes, moods, and visual style.
NLP Sentiment Analysis
Transformer models analyze reviews to extract emotional tones, pacing preferences, and thematic depth.
Hybrid Ranking
Collaborative and content-based signals merge into a single ranked list tuned to the user's current mood.
Discovery, reimagined
Natural language mood-based search
Tell us you want 'something like a warm hug on a rainy day' and we'll understand
Collaborative filtering with 50M+ ratings
Find people with similar taste and discover what they loved
Deep content analysis of plot and themes
NLP parsing of reviews, synopses, and critical analysis
Personalized watchlists with smart sorting
Your queue, intelligently organized by mood and availability
Social features for sharing recommendations
Create and share curated lists with friends
Integration with major streaming platforms
See where to watch across Netflix, Prime, Hulu, and more
Built for scale and speed
ML Pipeline
- TensorFlow for deep learning models
- Collaborative filtering with matrix factorization
- Transformer-based NLP for text understanding
- Real-time model inference at scale
Data Infrastructure
- Neo4j graph database for relationships
- FastAPI for high-performance endpoints
- AWS infrastructure with auto-scaling
- 500K+ movies indexed and searchable
Numbers that speak
Finally, a recommendation engine that gets me. I described wanting 'something like a warm hug on a rainy day' and it suggested the perfect film.