Beyond Pairwise Learning-To-Rank At Airbnb (2505.09795v3)

Abstract: There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But here's the catch-no algorithm can achieve all three at the same time. We call this limitation the SAT theorem for ranking algorithms. Given the dilemma, how can we design a practical system that meets user needs? Our current work at Airbnb provides an answer, with a working solution deployed at scale. We start with pairwise learning-to-rank (LTR) models-the bedrock of search ranking tech stacks today. They scale linearly with the number of items ranked and perform strongly on metrics like NDCG by learning from pairwise comparisons. They are at a sweet spot of performance vs. cost, making them an ideal choice for several industrial applications. However, they have a drawback-by ignoring interactions between items, they compromise on accuracy. To improve accuracy, we create a "true" pairwise LTR model-one that captures interactions between items during pairwise comparisons. But accuracy comes at the expense of scalability and total order, and we discuss strategies to counter these challenges. For greater accuracy, we take each item in the search result, and compare it against the rest of the items along two dimensions: (1) Superiority: How strongly do searchers prefer the given item over the remaining ones? (2) Similarity: How similar is the given item to all the other items? This forms the basis of our "all-pairwise" LTR framework, which factors in interactions across all items at once. Looking at items on the search result page all together-superiority and similarity combined-gives us a deeper understanding of what searchers truly want. We quantify the resulting improvements in searcher experience through offline and online experiments at Airbnb.