Complex multistage recommender systems that utilize multiple stages of ranking models and non-model parameters at different stages are used to identify a set of items as output, e.g., advertisements to be delivered by an advertising network. This disclosure describes a framework to optimize the non-model parameters to improve recall, defined based on items delivered as output in comparison to groundtruth items. The optimization can be performed offline, using a simulation that takes as input candidate items and labels of items that are known positives. The optimization can improve the quality of recommendations and can reduce computational cost.
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Anonymous, "End-to-end Optimization of Multistage Recommender Systems", Technical Disclosure Commons, (May 29, 2020)