I quickly read through the paper. One thing to note is that they use the Frobenius norm (at least I suppose this from the index F) for the matrix factorization. That is for their learning algorithm. Then, they use the cosine-similarity to evaluate. A metric that wasn't used in the algorithm.

This is a long-standing question for me. Theoretically, I should use the CS in my optimization and then also in the evaluation. But I haven't tested this empirically.

For example, there is sperical K-meams that clusters the data on the unit sphere.