Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences. This is often more effective than pointwise re-ranking models that directly predict a relevance value for each document. However, the high inference overhead of pairwise models limits their practical application: usually, for a set of $k$ documents to be re-ranked, preferences for all $k^2-k$ comparison pairs excluding self-comparisons are aggregated. We investigate whether the efficiency of pairwise re-ranking can be improved by sampling from all pairs. In an exploratory study, we evaluate three sampling methods and five preference aggregation methods. The best combination allows for an order of magnitude fewer comparisons at an acceptable loss of retrieval effectiveness, while competitive effectiveness is already achieved with about one third of the comparisons.

翻译：Pair-witter重新排序模型预测两种文件中哪一种与查询更相关,然后从这种偏好中汇总最后排名。这往往比直接预测每份文件相关价值的尖锐重排位模型更为有效。然而,双向模型的高推论间接成本限制了其实际应用:通常,要重新排序一套K$文件,就要对除自我比较之外的所有美元2K元比较对的偏好进行汇总。我们调查是否可以通过从所有对子取样来提高对等重新排序的效率。在一项探索性研究中,我们评估了三种抽样方法和五种优惠汇总方法。最佳组合使得在可接受的检索效率丧失的情况下可以减少规模的比较,而大约三分之一的比较已经实现了竞争力。