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Subject [GitHub] [systemml] deutschmn opened a new pull request #925: Implementation of Neural Collaborative Filtering (NCF)
Date Thu, 28 May 2020 13:52:27 GMT

deutschmn opened a new pull request #925:

   This pull request adds an implementation of Neural Collaborative Filtering (NCF) using
the SystemDS NN library. 
   The concept is based on [this paper ](
and implements a fixed architecture – details are described in the `` of the examples
for the NN library. 
   The implementation includes: 
   - `NCF.dml`: train, predict and evaluate an NCF model
   - `ncf-dummy-data.dml` train the model with synthetic data (fast)
   - `ncf-real-data.dml` train the model with the MovieLens data set (takes some time)
   - `Example - Neural Collaborative Filtering.ipynb`: a Jupyter notebook to download the
data, prepare it, call the training scripts and plot the training history
   Some remarks:
   - This implementation has a fixed network architecture for three dense layers. I was thinking
about making it dynamic, but this would require all weights and biases to be stored in untyped
lists, which I feared would kill performance. 
   - For now the implementation does not add any tests, as it's rather a demonstration of
how SystemDS can be used and not a part that can easily be reused for a different use case.
Also, I wasn't sure what to test against, since the network doesn't have any clear expected
   - I now implemented it as a script file rather than as a built-in functions, as built-ins
would cross-reference each other and also make use of the NN-library, which itself isn't a
built-in. If these issues are resolved, it could, however, be ported over to a built-in.

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