ULTRA: An Unbiased Learning To Rank Algorithm Toolbox
Anh Tu Tran, Tao Yang, Qingyao Ai · 2021
Learning to rank system has become an important aspect of our daily life. However, the implicit user feedback that is used to train many learning to rank models is usually noisy and suffers from user bias (i.e., position bias). Thus, obtaining unbiased model using biased feedback has become an important research field for IR. Existing studies on unbiased learning to rank (ULTR) can be generalized into two families-algorithms that attain unbiasness with logged data, offline learning, and algorithms that achieve unbiasness by estimating unbiased parameters with real-time user interactions, namely online learning. While there exist many algorithms from both families, there lacks a unified way to compare and benchmark them. As a result, it can be challenging for researchers to choose the right technique for their problems or for people who are new to the field to learn and understand existing algorithms. To solve this problem, we introduced ULTRA, which is a flexible, extensible, and easily configure ULTR toolbox. Its key features include support for multiple ULTR algorithms with configurable hyper parameters, a variety of built-in click models that can be used separately to simulate clicks, different ranking model architectures and evaluation metrics, and simple learning to rank pipeline creation. In this paper, we discuss the general framework of ULTR, briefly describe the algorithms in ULTRA, detail the structure, and pipeline of the toolbox. We experimented on all the algorithms supported by ULTRA and showed that the toolbox performance is reasonable. Our toolbox is an important resource for researchers to conduct experiments on ULTR algorithms with different configurations as well as testing their own algorithms with the supported features.