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Data and algorithm sharing is an imperative part of data and AI-driveneconomies. The efficient sharing of data and algorithms relies on the activeinterplay between users, data providers, and algorithm providers. Althoughrecommender systems are known to effectively interconnect users and items ine-commerce settings, there is a lack of research on the applicability ofrecommender systems for data and algorithm sharing. To fill this gap, weidentify six recommendation scenarios for supporting data and algorithmsharing, where four of these scenarios substantially differ from thetraditional recommendation scenarios in e-commerce applications. We evaluatethese recommendation scenarios using a novel dataset based on interaction dataof the OpenML data and algorithm sharing platform, which we also provide forthe scientific community. Specifically, we investigate three types ofrecommendation approaches, namely popularity-, collaboration-, andcontent-based recommendations. We find that collaboration-based recommendationsprovide the most accurate recommendations in all scenarios. Plus, therecommendation accuracy strongly depends on the specific scenario, e.g.,algorithm recommendations for users are a more difficult problem than algorithmrecommendations for datasets. Finally, the content-based approach generates theleast popularity-biased recommendations that cover the most datasets andalgorithms.

Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing