Movie recommendation system using Restricted Boltzmann Machines (RBM) on MovieLens dataset.
SASRec Movie Recommendation API on AWS. Self-attention sequential recommender with 2 attention blocks, 50-dim embeddings. NDCG@10: 58.11%, Hit@10: 78.49%. 1.81ms inference, 8,366 req/s on CPU. Deployed on EC2 with Docker, Streamlit dashboard. Trained on MovieLens-1M.
Collaborative-filtering recommender built from scratch — Truncated SVD + SGD-trained bias terms in NumPy, with embedding probing for implicit demographic signal
Sequential Recommendation Model on MovieLensM1
Movie recommendation system using collaborative filtering, matrix factorization (SVD), FastAPI and MovieLens data.
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