The second phase has ended and at this point I think we are very much close to having a basic implementation of
NCF in mlpack. I spend this week mainly making modifications to the
GetRecommendations() method and creating the
EvaluateModel() method. They have been completed and pushed.
EvaluateModel() now evaluates the model on two parameters,
hit ratio and
RMSE. But the
Train() method hasn't been completed yet, slight modifications are still necessary to add
Evaluate() in NCF, and work on it is ongoing with input from Marcus. So the entire class can be tested once
Train() is complete.
Right now I am also working on
ncf_main, this will hopefully help us use
NCF from command line interface too. By end of this week I intend to have a proper trainable
NCF so that all methods can be tested and the network evaluated. There might be some debugging necessary after
Train() is completed. But apart from that the basic class, along with CLI is expected to be ready by end of the week.
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