An AI-powered system that blends two Letterboxd profiles and recommends movies both users are likely to enjoy. The project focuses on compatibility, joint taste discovery, and diversity-aware recommendations, which better reflects real-world decision-making compared to traditional single-user recommender systems.
👉 Hugging Face Space: https://huggingface.co/spaces/celalibr/letterboxd-blend-recommender
Upload two Letterboxd export ZIP files and explore:
- Compatibility score
- Shared taste analysis
- Joint genre preferences
- Mood-based movie recommendations
Most recommender systems are built for individuals. However, in real life, people often choose movies together: couples, friends, families.
This project introduces a couple-aware recommender system that:
- Measures compatibility
- Models joint preferences
- Balances exploration and exploitation
- Encourages diversity in recommendations
The system evaluates similarity between two users using:
- Jaccard overlap of watched films
- Pearson correlation of ratings
- Mean absolute rating difference
These are combined into a weighted compatibility score.
Each user’s taste is learned from:
- Highly rated movies
- Genre distributions
- Joint preference blending
Users can select moods such as:
- Romantic
- Fun
- Dark
- Epic
The system dynamically reweights genre preferences.
To avoid repetitive mainstream recommendations:
- Popularity-aware scoring
- Novelty bonus for less obvious films
We use Maximal Marginal Relevance (MMR) to ensure:
- Genre diversity
- Temporal diversity
- Reduced redundancy
The model uses real-world movie metadata:
- Genres
- Ratings
- Vote counts
- Runtime
- Release year
This allows higher-quality and more robust recommendations.
The pipeline includes:
- Data ingestion from Letterboxd export files
- IMDb dataset matching and enrichment
- User profiling
- Joint preference modeling
- Ranking optimization
- Diversity-aware recommendation
- Mood-based personalization
- Python
- Pandas
- NumPy
- Recommender systems
- Ranking algorithms
- Gradio
- Hugging Face Spaces
- IMDb dataset
This project demonstrates:
- Real-world recommender system design
- Personalization at the group level
- Product thinking
- Ranking and diversity optimization
- Scalable data pipelines
- Deployment and MLOps mindset
It goes beyond typical ML projects by combining:
- User behavior
- Metadata
- Business logic
- User experience
Planned upgrades include:
- Semantic NLP embeddings from reviews
- SBERT-based similarity
- Deep learning ranking models
- Collaborative filtering
- Cold-start solutions
- Personality-aware recommendation
Feel free to fork, experiment, and extend the system.
If you find this interesting, feel free to connect with me on LinkedIn.
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