Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting different objective functions that can effectively drive the algorithm towards promising areas of the search space. We enrich the iterative process followed by the learning algorithm in two ways: (1) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (2) by designing a learning path across different loss functions that can capture different signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically significant margins.

学习排名（LtR）技术利用查询文档相关性的评估样本来学习有效的排名功能，该功能能够利用隐藏在用于表示查询和文档的功能中的嘈杂信号。在本文中，我们探讨了如何增强最先进的LambdaMartLtR算法通过在训练过程中集成有关底层用户交互模型的显式知识以及针对不同目标函数的可能性，这些目标函数可以有效地将算法推向有希望的搜索空间区域。我们通过两种方式来丰富学习算法所遵循的迭代过程：（1）通过考虑基于查询的复杂用户动态，而不是简单地将增益按排名位置打折；（2）通过设计跨不同损失函数的学习路径，可以在训练数据中捕获不同信号。我们对可公开获得的数据集进行的广泛实验表明，提出的解决方案可以通过统计上显着的幅度改善各种排名质量度量。