Learning to efficiently rank

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Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “However, just to add to the confusion, BM25 doesn’t necessarily imply bag-of-words queries, as there are extensions of BM25 to phrase queries, for example, Wang et al. [2011]”
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking (2020, methodology): “This includes applying a temporal constraint on the number of features that are selected for a re-ranking model [35], incorporating an efficiency metric in the training of linear rankers [34], and comparing the effectiveness and efficiency of various learning-to-rank algorithms [4].”
A cascade ranking model for efficient ranked retrieval (2011, background): “, optimized for effectiveness only with no efficiency considerations); a previously best-known model that jointly optimizes for both ranked effectiveness and efficiency by reducing the number of features computed at query time (which we call “FeaturePrune”) [27]; and the basic query-likelihood model”
Quality versus efficiency in document scoring with learning-to-rank models (2016, unknown): “[18, 19, 20] deeply investigate different efficiency aspects of the ranking pipeline.”