Force-Directed Graph Visualization Recommendation Engine: ML or Physics Simulation?
Analysis
This post describes a novel recommendation engine that blends machine learning techniques with a physics simulation. The core idea involves representing images as nodes in a force-directed graph, where computer vision models provide image labels and face embeddings for clustering. An LLM acts as a scoring oracle to rerank nearest-neighbor candidates based on user likes/dislikes, influencing the "mass" and movement of nodes within the simulation. The system's real-time nature and integration of multiple ML components raise the question of whether it should be classified as machine learning or a physics-based data visualization tool. The author seeks clarity on how to accurately describe and categorize their creation, highlighting the interdisciplinary nature of the project.
Key Takeaways
- •Hybrid approach combining ML and physics simulation for recommendations.
- •Leverages LLMs for scoring and reranking candidates.
- •Real-time interaction and state persistence across sessions.
“Would you call this “machine learning,” or a physics data visualization that uses ML pieces?”