Preference learning (PL) is an emerging sub eld of machine learning, which deals with the induction of preference models from observed preference information [3]. Such models are typically used for prediction purposes, for example to predict context-dependent preferences of individuals on various choice alternatives. Depending on the representation of preferences, individuals, alternatives, and contexts, a large variety of preference models and problems are conceivable. We developed a software framework o ering tools and algorithms for solving preference learning problems.1 While software frameworks for core machine learning problems such as classi cation abound, we are not aware of any comprehensive library of tools for preference learning. In fact, existing libraries are essentially restricted to one or two types of PL problems (e.g. [2], [6], [5], [4], [1]). Our framework, called jPL, is implemented in Java. It is based on a uni ed data format, the Generic Preference Representation Format (GPRF), which is suitable for modeling data related to di erent kinds of preference learning problems. This also includes a dataset transformer, which converts data from several existing formats to GPRF. As problem classes, the framework currently supports collaborative ltering, instance ranking, label ranking, multilabel classifcation, object ranking, ordinal classi cation, and rank aggregation out of the box, with at least two algorithms being implemented for each problem. It provides a convenient command line interface as well as an API, both allowing one to con gure the system using json les. The whole framework was developed in a quite generic way, so as to allow other problems and algorithms to be added easily. Our framework also supports the evaluation and comparison of di erent methods in terms of standard validation techniques, and includes a set of commonly used loss functions. Just like the framework as a whole, the evaluation component is easily extensible by new evaluation techniques and loss functions.
3. Johannes Furnkranz and Eyke Hullermeier. Preference learning: An introduction.
In Preference learning, pages 1{17. Springer, 2010. 4. Nicholas Mattei and Toby Walsh. Pre ib: A library for preferences http://www. pre ib. org. In International Conference on Algorithmic DecisionTheory, pages 259{270. Springer, 2013. 5. Jesse Read, Peter Reutemann, Bernhard Pfahringer, and Geo Holmes. Meka: a multi-label/multi-target extension to weka. The Journal of Machine Learning Research, 17(1):667{671, 2016. 6. Grigorios Tsoumakas, Eleftherios Spyromitros-Xiou s, Jozef Vilcek, and Ioannis Vlahavas. Mulan: A java library for multi-label learning. Journal of Machine Learning Research, 12(Jul):2411{2414, 2011.