Recent years have seen a shift from a pattern mining process that has users define constraints beforehand, and sift through the results afterwards, to an interactive one. This new framework depends on exploiting user feedback to learn a quality function for patterns. Existing approaches have a weakness in that they use static pre-defined low-level features, and attempt to learn independent weights representing their importance to the user. As an alternative, we propose to work with more complex features derived directly from the pattern ranking imposed by the user. Those features are used to learn weights to be aggregated with low-level features and help to drive the quality function in the right direction. Experiments on UCI datasets show that using higher-complexity features leads to the selection of patterns that are better aligned with a hidden quality function while being competitively fast when compared to state-of-the-art method