Providing recommendation to agents (e.g. people or organizations) regarding whom they should collaborate with in order to reach some objective is a recurring problem in a wide range of domains. It can be useful for instance in the context of collaborative machine learning, grouped purchases, and group holidays. This problem has been modeled by hedonic games, but this generic formulation cannot easily be used to provide efficient algorithmic solutions. In this work, we define a class of hedonist games that allows us to provide an algorithmic solution to the collaboration recommendation problem by means of a clustering algorithm. We evaluate our algorithm, theoretically and experimentally and show that it performs better than other clustering algorithms in this context.