Evaluating recommender systems for AI-driven data science
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We present a free and open-source platform to allow researchers to easily apply supervised machine learning to their data. A key component of this system is a recommendation engine that is bootstrapped with machine learning results generated on a repository of open-source datasets. The recommendation system chooses which analyses to run for the user, and allows the user to view analyses, download reproducible code or fitted models, and visualize results via a web browser. The recommender system learns online as results are generated. In this paper we benchmark several recommendation strategies, including collaborative filtering and metalearning approaches, for their ability to learn to select and run optimal algorithm configurations for various datasets as results are generated. We find that a matrix factorization-based recommendation system learns to choose increasingly accurate models from few initial results.
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