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This repository provides a modular template for building recommender systems in Python using **implicit feedback data**. It is designed to streamline experimentation of recommendation models with a modern ML stack.
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This repository provides a modular template for building recommender systems in Python using **implicit feedback data**. It is designed to streamline experimentation of recommendation models with a modern ML stack. Two neural-based models are implemented: Matrix Factorization and MLP based on one of two user representations: user represented by its embedding or user represented by histories of clicked items (item embeddings).
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### 🔧 Tech Stack
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-**PyTorch Lightning** – for scalable and structured model training
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