Many collaborative filtering recommender systems collect and use users' explicitly entered preferences in the form of ratings for items. However, in many real world scenarios, this form of feedback can be difficult to obtain or unavailable (e.g., news portals). In this case recommendations must be built by leveraging more abundant implicit feedback data, which only indirectly signal users' preferences or opinions. A record in such datasets is a result of an action performed by a user on an item (e.g., the item was clicked or viewed). State-of-the-art implicit feedback recommender systems predict whether the user will act on a target item and interpret this prediction as a discovered preference for the item. These models are trained by observations of user actions of one single type. For instance, they predict that a user will watch a video using a dataset of observed video watch actions. In this paper we conjecture that multiple types of user actions may be jointly exploited to predict one target type of actions. We present a general prediction model (MMF - Multiple action types Matrix Factorization) that implements this conjecture and we illustrate some practical examples. The empirical evaluation of MMF, which was conducted on a large real world dataset, shows that using multiple actions is beneficial and it can outperform a state-of-the-art implicit feedback model that uses only the target action data.