This work proposes and evaluates a novel approach to determine interesting category for ranked lists using $\nu$-SVM. We identify three characteristics (features), entropy, unlikability and peculiarity and show how to train a classifier on these features using a set of Wikipedia tables. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the correctness of our approach.