The current MatrixFactorizationModel API does not directly support item-to-item similarity computations. Therefore, we will need to create our own code to do this.
We will use the cosine similarity metric, and we will use the jblas linear algebra library (a dependency of MLlib) to compute the required vector dot products. This is similar to how the existing predict and recommendProducts methods work, except that we will use cosine similarity as opposed to just the dot product.
We would like to compare the factor vector of our chosen item with each of the other items using our similarity metric. In order to perform linear algebra computations, we will first need to create a vector object ...
