The previous chapter showed you how to evaluate a model. The performance indices are useful to compare different models and/or parameters. Applying different techniques on the same data, we can compare a performance index to pick the most appropriate recommender. Since there are different evaluation metrics, there is no objective way to do it.
The starting point is the k-fold evaluation framework that we defined in the previous section. It is stored inside
In order to compare different models, we first need to define them. Each model is stored in a list with its name and parameters. The components of the list are as follows:
name: This is the model name.
param: This is a list with its ...