[ TSUKUYOMI_ENGINE_ACTIVE ]

MISSION: DELIVER PERSONALIZED MOVIE RECOMMENDATIONS WITH EXTREME MEMORY EFFICIENCY.

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// OPTIMIZATION_PROTOCOL //

The core achievement of this project was the drastic reduction in model size without sacrificing recommendation quality.

[ 8-BIT_QUANTIZATION ]

• Technique: Converted model weights from 32-bit floating point (FP32) to 8-bit integers (INT8).
• Result: Reduced model size from 4 GB to < 300 MB.
• Precision: Maintained 98% of the original model’s accuracy (Top-10 recommendation hit rate).

IMPACT: Allowed the model to run on standard consumer hardware and edge devices with limited RAM.

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// SYSTEM_ARCHITECTURE //

[ HYBRID_FILTERING ]

The engine uses a collaborative filtering algorithm that clusters users with similar viewing patterns.

1. User Clustering: K-Means clustering to group users based on genre preferences.
2. Similarity Matrix: Cosine similarity to find nearest neighbors within clusters.
3. Cold Start: Fallback to popularity-based recommendations for new users.

graph TD
    User[User Interaction] -->|Ratings| DB[(MySQL Database)]
    DB -->|Fetch Data| Engine[Recommender Engine]
    Engine -->|Quantized Model| Inference[Generate List]
    Inference -->|Top 10| UI[Web Interface]
    
    subgraph Optimization
    Engine
    Inference
    end

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// FULL_STACK_INTEGRATION //

• Backend: Python + Flask serving the API.
• Database: MySQL with 3NF normalization for efficient query performance.
• Security: Encrypted POST requests for all API interactions.
• Frontend: Responsive web interface for browsing movies and submitting ratings.

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// FEATURES //

• Visual Neural Network: Visualization of movie relationships.
• Trailer Fetching: Automated YouTube trailer integration.
• Offline Mode: Cached recommendations for low-connectivity environments.