Neural Collaborative Filtering - Week 8

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 Gradient() and 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.