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Eric Chung PRO

DawnC
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AI & ML interests

Computer Vision, LLM, Hybrid Architectures, MultiModel, Reinforcement Learning

Recent Activity

replied to their post 3 days ago
PawMatchAI — Smarter, Safer, and More Thoughtful Recommendations 🐕✨ 🐾 Recommendation system update — deeper reasoning, safer decisions Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness. Key technical improvements: - SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family) - Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed -Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%) -Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term. What's improved? - 🎯 Clearer separation of must-have safety constraints versus flexible preferences - 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives - 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting What's next? - 🐕 Expanding behavioral and temperament analysis dimensions - 🐾 Extension to additional species with transfer learning - 📱 Mobile-optimized deployment for easier access - 🧩 Enhanced explainability showing why specific breeds are recommended 👉 Try PawMatchAI: https://huggingface.co/spaces/DawnC/PawMatchAI #AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
posted an update 5 days ago
PawMatchAI — Smarter, Safer, and More Thoughtful Recommendations 🐕✨ 🐾 Recommendation system update — deeper reasoning, safer decisions Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness. Key technical improvements: - SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family) - Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed -Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%) -Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term. What's improved? - 🎯 Clearer separation of must-have safety constraints versus flexible preferences - 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives - 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting What's next? - 🐕 Expanding behavioral and temperament analysis dimensions - 🐾 Extension to additional species with transfer learning - 📱 Mobile-optimized deployment for easier access - 🧩 Enhanced explainability showing why specific breeds are recommended 👉 Try PawMatchAI: https://huggingface.co/spaces/DawnC/PawMatchAI #AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
updated a Space 5 days ago
DawnC/PawMatchAI
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