By combining practical relevance with novel types of prediction problems, the learning from/of preferences has recently received a lot of attention in the machine learning literature. Just as other types of complex learning tasks, preference learning deviates strongly from the standard problems of classification and regression. It is particularly challenging because it involves the prediction of complex structures, such as weak or partial order relations, rather than single values. This article

more »

... aims at conveying a first idea of typical preference learning problems. To this end, two particular learning scenarios will be sketched, namely learning from label preferences and learning from object preferences. Both scenarios can be handled in two fundamentally different ways: by evaluating individual candidates (using a utility function) or by comparing competing candidates (using a binary "is preferred to" predicate).