Domain adaptation aims to transfer the knowledge acquired by models trained on (data-rich) source domains to (low-resource) target domains, for which a popular method is invariant representation learning. While they have been studied extensively for classification and regression problems, how they apply to ranking problems, where the data and metrics have a list structure, is not well understood. Theoretically, we establish a domain adaptation generalization bound for ranking under listwise metrics such as MRR and NDCG. The bound suggests an adaptation method via learning list-level domain-invariant feature representations, whose benefits are empirically demonstrated by unsupervised domain adaptation experiments on real-world ranking tasks, including passage reranking. A key message is that for domain adaptation, the representations should be analyzed at the same level at which the metric is computed, as we show that learning invariant representations at the list level is most effective for adaptation on ranking problems.

翻译：适应领域的目的是将受过(数据丰富)源域培训的模型获得的知识转移到(低资源)目标领域,而对于这些领域,一种流行的方法是无差异的代谢学习;虽然为分类和回归问题对模型进行了广泛研究,但对于如何将模型应用于排名问题、数据和衡量标准具有列表结构的地方,没有很好地理解。理论上,我们为在列表性指标(如MRR和NDCG)下进行排序确定了一种域性适应通用。约束表明,通过学习列表级域-异差特征表来采用适应方法,其好处通过在实际世界排名任务(包括排位)上进行未经监督的域性适应实验得到实证证明。一项关键信息是,对于域的适应,应当按照计算指标的同一水平分析各种代言,因为我们表明,在列表一级学习异性表示对于在排序问题上的适应最为有效。