Stochastic Retrieval-Conditioned Reranking
Topics: Information Gain, Learning-to-rank, Ranking, Reranking, User Signals
The Google research paper “Stochastic Retrieval-Conditioned Reranking” explores how search engines can improve ranking by optimizing both retrieval and reranking stages together. It challenges the traditional assumption that recall optimization in the first retrieval stage guarantees better ranking results. Instead, it introduces a theoretical framework called Expected Reranking Performance Conditioned on First-stage Retrieval (ECR), which shows that precision at retrieval is more important than recall. The authors propose a new loss function that leads to better ranking performance on multiple datasets and provide a practical optimization method for training reranking models.
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