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| title: Optimised Amazon RecSys | |
| emoji: 🐢 | |
| colorFrom: pink | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 6.4.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: HNSW Quantisation ONNX supported Optimised Amazon RecSys | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| * Engineered a Two-Tower (Bi-Encoder) semantic retrieval system (MPNet), fine-tuned on 826k+ user–item interactions using supervised contrastive learning (Multiple Negatives Ranking Loss), achieving Recall@10 of 0.2946 and strong query–product alignment. | |
| * Scaled vector search from linear O(N) to logarithmic O(log N) by replacing brute-force FAISS IndexFlatIP with FAISS HNSW approximate nearest neighbor indexing, enabling real-time Top-K retrieval. | |
| * Accelerated inference by 5× (32 ms → 6 ms) by migrating from PyTorch FP32 to ONNX Runtime with Int8 quantization, achieving a 4× reduction in model size and enabling cost-efficient CPU deployment. | |
| * Optimized data and training pipelines using Automatic Mixed Precision (AMP) and implemented O(1) binary metadata caching, ensuring instant retrieval of multi-modal assets (images and videos) without data loading bottlenecks. | |
| * Deployed a production-ready recommendation microservice on Hugging Face Spaces with a custom A/B benchmarking dashboard, validating the 5× latency reduction against the baseline model in real time. |