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Recommender systems have become an integral part of online platforms,providing personalized recommendations for purchases, content consumption, andinterpersonal connections. These systems consist of two sides: the producerside comprises product sellers, content creators, or service providers, etc.,and the consumer side includes buyers, viewers, or customers, etc. To optimizeonline recommender systems, A/B tests serve as the golden standard forcomparing different ranking models and evaluating their impact on both theconsumers and producers. While consumer-side experiments is relativelystraightforward to design and commonly employed to assess the impact of rankingchanges on the behavior of consumers (buyers, viewers, etc.), designingproducer-side experiments for an online recommender/ranking system is notablymore intricate because producer items in the treatment and control groups needto be ranked by different models and then merged into a unified ranking to bepresented to each consumer. Current design solutions in the literature are adhoc and lacking rigorous guiding principles. In this paper, we examinelimitations of these existing methods and propose the principle of consistencyand principle of monotonicity for designing producer-side experiments of onlinerecommender systems. Building upon these principles, we also present asystematic solution based on counterfactual interleaving designs to accuratelymeasure the impacts of ranking changes on the producers (sellers, creators,etc.).

Producer-Side Experiments Based on Counterfactual Interleaving Designs for Online Recommender Systems