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 eval_sets.
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 parameters. It can be NULL, if all the parameters are left at their defaults.
For instance, that's how we can define an item-based collaborative filtering by setting the k parameter to 20:
list(name = "IBCF", param = list(k = 20))
In order to evaluate...