We develop a recommendation system, for the online casino, which will suggest the next game to a player based on the games previously selected. The system is based on a combination of unsupervised and supervised learning. Unsupervised learning is used for hierarchical clustering of games which helps solve the problem of large number of available games on the online casino. In turn, we use supervised learning based on Markov chain transition matrices to infer the probabilities of transition between game clusters. We empirically show that the recommendation system allows for accurate prediction of the cluster of the next game for an observed player. A comparison with simple, baseline algorithms confirms the superiority of
the proposed approach. It should be emphasized that the developed system does not recommend individual games but a cluster of games. The selection of a particular game from the cluster might be left to the online casino software. Alternatively, the selection can be based on the clustering of players base on their gaming patterns.