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The notion of meta-mining has appeared recently and extends the traditionalmeta-learning in two ways. First it does not learn meta-models that providesupport only for the learning algorithm selection task but ones that supportthe whole data-mining process. In addition it abandons the so called black-boxapproach to algorithm description followed in meta-learning. Now in addition tothe datasets, algorithms also have descriptors, workflows as well. For thelatter two these descriptions are semantic, describing properties of thealgorithms. With the availability of descriptors both for datasets and datamining workflows the traditional modelling techniques followed inmeta-learning, typically based on classification and regression algorithms, areno longer appropriate. Instead we are faced with a problem the nature of whichis much more similar to the problems that appear in recommendation systems. Themost important meta-mining requirements are that suggestions should use onlydatasets and workflows descriptors and the cold-start problem, e.g. providingworkflow suggestions for new datasets.  In this paper we take a different view on the meta-mining modelling problemand treat it as a recommender problem. In order to account for the meta-miningspecificities we derive a novel metric-based-learning recommender approach. Ourmethod learns two homogeneous metrics, one in the dataset and one in theworkflow space, and a heterogeneous one in the dataset-workflow space. Alllearned metrics reflect similarities established from the dataset-workflowpreference matrix. We demonstrate our method on meta-mining over biological(microarray datasets) problems. The application of our method is not limited tothe meta-mining problem, its formulations is general enough so that it can beapplied on problems with similar requirements.

Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining