End-to-end listwise machine learning ranking models for information retrieval systems

用于信息检索系统的端到端列表机器学习排名模型

• 批准号: 2605144
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605144
• 关键词: End; end; listwise; machine; learning

Machine learned models are prevalent in a number of recommender system and information retrieval use cases. Often the models are trained using single data points (pointwise), or using pairs of instances of different quality (pairwise). However, previous research has showed that such pointwise or pairwise loss functions can be inferior to listwise loss functions, which aim to get an entire ranking correct. Yet, many recent deep neural techniques, such as BERT text ranking models, continue to be trained in pairwise manner. In this research, the student will investigate ways to adapt listwise loss functions to modern deep learned frameworks; Evaluation can be conducted using existing recommendation and search datasets.

机器学习模型在许多推荐系统和信息检索用例中普遍存在。通常，模型使用单个数据点（逐点）或使用不同质量的成对实例（成对）进行训练。然而，以前的研究表明，这种逐点或成对损失函数可能不如列表损失函数，后者旨在获得正确的整个排名。然而，许多最近的深度神经技术，如BERT文本排名模型，继续以成对的方式进行训练。在这项研究中，学生将研究如何使列表损失函数适应现代深度学习框架;可以使用现有的推荐和搜索数据集进行评估。