Automatically tuning SVD (NNI in Recommenders)

In this tutorial, we first introduce a github repo Recommenders. It is a repository that provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. It has various models that are popular and widely deployed in recommendation systems. To provide a complete end-to-end experience, they present each example in five key tasks, as shown below:

The fourth task is tuning and optimizing the model’s hyperparameters, this is where NNI could help. To give a concrete example that NNI tunes the models in Recommenders, let’s demonstrate with the model SVD, and data Movielens100k. There are more than 10 hyperparameters to be tuned in this model.

This Jupyter notebook provided by Recommenders is a very detailed step-by-step tutorial for this example. It uses different built-in tuning algorithms in NNI, including Annealing, SMAC, Random Search, TPE, Hyperband, Metis and Evolution. Finally, the results of different tuning algorithms are compared. Please go through this notebook to learn how to use NNI to tune SVD model, then you could further use NNI to tune other models in Recommenders.