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TopK Recommendation System using Neural Collaborative Filtering

This example demonstrates how to train a recommendation system with implicit feedback on the MovieLens 100K (ml-100k) dataset using a Neural Collaborative Filtering model. This model trains on binary information about whether or not a user interacted with a specific item. To target the models for an implicit feedback and ranking task, we optimize them using sigmoid cross entropy loss with negative sampling.

Setup

To begin, you'll need the latest version of Swift for TensorFlow installed. Make sure you've added the correct version of swift to your path. To train the model, run:

cd swift-models
swift run NeuMF-MovieLens