Abstract
Online recommendation task has been recognized as a Multi-Armed Bandit (MAB) problem. Despite the recent advances, there still needs to be more consensus on the best practices to evaluate such bandit solutions. Recently, we observed two complementary frameworks that allow us to evaluate bandit solutions more accurately: iRec and OBP. The first has a complete set of datasets, metrics, and MAB models implemented, allowing only offline evaluations of these solutions. However, the second is limited to a few bandit solutions with more current metrics and methodologies, such as counterfactuals. In this work, we propose and evaluate an integration between these two frameworks, demonstrating the potential and richness of analyzes that can be carried out from this combination.
This work was partially funded by CNPq, CAPES, FINEP and Fapemig.
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Notes
- 1.
Available at https://github.com/YanAndrade61/iRec-OBP.
- 2.
Available at https://github.com/irec-org.
- 3.
Available at https://github.com/YanAndrade61/iRec-OBP.
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Andrade, Y. et al. (2023). Integrating Counterfactual Evaluations into Traditional Interactive Recommendation Frameworks. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_41
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