Preference learning in general and label ranking in particular have been applied successfully for meta-learning problems in the past [1, 4, 3]. The bene ts of incorporating additional feature descriptions of alternatives in the context of preference learning have recently been shown for the dyad ranking framework [6]. Additional descriptions in the form of feature vectors are known in the recommender systems domain, too, where they are typically called sideinformation and used for tackling cold-start problems. These problems refer to situations where preference indicators (e.g., ratings) for new users or new items are not yet available (see Figure 1). In these situations, side-information helps by putting existing and new entities into relation. In this work, we make use Side-Information: Algorithm Parameters New Problems
New Parameters
New Parameters
& new Problems
Fig. 1. Three kinds of cold-start problems are shown. They are characterized in that
no preference indicators are available for algorithms or problems. Side-information can
help in these situations for inferring preferences and thus recommendations.
of dyad ranking to predict a good ranking of candidate algorithms
contextualized by problem instances, assuming that algorithms exhibit a representation
in terms of a feature description. By generalizing over both, attributes of
problems as well as algorithms, it becomes possible to tackle cold-start scenarios in
which predictions are sought for algorithms that never occurred in the
training data. A similar viewpoint towards meta-learning has been taken in [
In the rst variant (the \reference" situation), a method is trained on data where the label Aj is part of the training set, whereas in the second variant (the \cold start" situation) the same method is trained on data where Aj is completely omitted. In addition to the Kendall value that is used to quantify the quality of a predicted ranking in relation to a ground truth ranking, the deviation between the predicted rank of Aj and the true rank is recorded.
In the reference and the cold start situation, the Kendall values are almost identical. Moreover, the average deviation from the true rank in the reference case is 5.653 and in the cold-start scenario 5.712. These are rst encouraging results. Future work could comprise experiments on further meta data sets and address the development of further approaches for cold-start problems.