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A/B Testing for Recommender Systems in a Two-sided Marketplace by Nandy, Preetam; Venugopalan, Divya; Lo, Chun; Chatterjee, Shaunak is a scholarly article available to read on EtoBox.

What is A/B Testing for Recommender Systems in a Two-sided Marketplace about?

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification. Our approach, called UniCoRn (Unifying Counterfactual Rankings), provides explicit control over the quality of the experiment and its computation cost. Further, we prove that our e

Author

Nandy, Preetam; Venugopalan, Divya; Lo, Chun; Chatterjee, Shaunak

Published

2021

Language

EN