Neural Collaborative Filtering Code Walkthrough - Recommender System

Theory is one thing. Implementation is where the rubber meets the road. Let's build the two-tower neural recommender from scratch. In this tutorial (Part 2), we implement the complete neural collaborative filtering pipeline in PyTorch. You'll learn how the "RecommenderDataset" class creates 2.5 million training examples from 500K purchases using intelligent negative sampling (the clever indexing trick that returns 1 positive + 4 negatives per batch), how feature normalization prevents scale imbalance in the neural network, and how categorical mappings convert departments and aisles into learnable embeddings. We walk through the "TwoTowerModel" architecture line by line: the user tower's embedding layer (78K users → 64 dimensions) combined with normalized behavioral features, the item tower's triple embedding system (product ID + department + aisle), the MLP layers with 128→64 neuron compression and ReLU activations, and the final dot product that produces similarity scores. You'll see the training loop with binary cross-entropy loss, early stopping to prevent overfitting, batch processing at 1024 examples per step, and how gradient descent over multiple epochs teaches both towers to align user preferences with product characteristics in shared embedding space. This is Part 2 - the complete code implementation. Watch Part 1 first for the architectural foundations, then come back here to see it built from scratch! Link to code: https://github.com/bnsreenu/Recommend... (RecSys 5 - Part 2)