Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are often noisy measurements of human behavior, e.g., product rating for product search. The coarse measurements make the ground truth ranking non-unique with respect to a single relevance criterion. To resolve ambiguity, it is desirable to train a model using many relevance criteria, giving rise to Multi-Label LTR (MLLTR). Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e.g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue. In this research, we leverage the Multi-Objective Optimization (MOO) aspect of the MLLTR problem and employ recently developed MOO algorithms to solve it. Specifically, we propose a general framework where the information from labels can be combined in a variety of ways to meaningfully characterize the trade-off among the goals. Our framework allows for any gradient based MOO algorithm to be used for solving the MLLTR problem. We test the proposed framework on two publicly available LTR datasets and one e-commerce dataset to show its efficacy.
翻译:在目前的信息检索系统中,尤其是搜索排名应用程序中,学习排名(LTR)技术是无处不在的。通常用于培训排名模型的查询项目关联性标签通常是对人的行为进行噪音的测量,例如产品搜索的产品评级。粗略的测量使得在单一相关标准方面,地面真理排名与单一相关标准无关。为解决模糊问题,有必要用许多相关标准来培训一个模型,从而产生多标签LTR(MLTR)。此外,它还制定了许多可能相互冲突但很重要的目标,以便同时优化,例如,在产品搜索中,可以根据产品质量和购买增加收入的可能性对排名模型进行培训。在这项研究中,我们利用MLLLTR问题的多客观优化(MOO)方面,并采用最近开发的MOO算法来解决该问题。具体地说,我们提出了一个总框架,从标签中收集的信息可以以各种方式结合,从而在目标中有意义地描述交易。我们的框架允许任何基于梯度的MOO值的模型,并允许在产品质量和购买增加收入的可能性的基础上进行培训。在这个研究中,我们利用了以多种梯度为基础的MOO算法来公开测试。