Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing

TL;DR

This paper explores how recommender systems can support data and algorithm sharing, introducing new scenarios and evaluating different recommendation approaches using a novel dataset from OpenML.

Contribution

It identifies six new recommendation scenarios for data and algorithm sharing and evaluates three recommendation methods, providing insights into their effectiveness and biases.

Findings

Collaboration-based recommendations are most accurate across scenarios.

Recommendation accuracy varies significantly by scenario.

Content-based recommendations are less biased and cover more items.

Abstract

Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender systems are known to effectively interconnect users and items in e-commerce settings, there is a lack of research on the applicability of recommender systems for data and algorithm sharing. To fill this gap, we identify six recommendation scenarios for supporting data and algorithm sharing, where four of these scenarios substantially differ from the traditional recommendation scenarios in e-commerce applications. We evaluate these recommendation scenarios using a novel dataset based on interaction data of the OpenML data and algorithm sharing platform, which we also provide for the scientific community. Specifically, we investigate three types of recommendation approaches, namely popularity-, collaboration-, and content-based recommendations. We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios. Plus, the recommendation accuracy strongly depends on the specific scenario, e.g., algorithm recommendations for users are a more difficult problem than algorithm recommendations for datasets. Finally, the content-based approach generates the least popularity-biased recommendations that cover the most datasets and algorithms.