Recommender Systems

This directory has a set of examples of how to build various kinds of recommender systems using MXNet. The sparsity of user / item data is handled through the embedding layers that accept indices as input rather than one-hot encoded vectors.

Examples

The examples are driven by notebook files.

• Matrix Factorization: linear and non-linear models
• Deep Structured Semantic Model (DSSM) for content-based recommendations

Negative Sampling

• A previous version of this example had an example of negative sampling. For example of negative sampling, please refer to: Gluon NLP Sampled Block

Acknowledgements

Thanks to xlvector for the first Matrix Factorization example that provided the basis for these examples.

MovieLens data from GroupLens.