RUCIR21 at the NTCIR-16 ULTRE Task
Year: 2022
Type: conference-paper
Abstract: The RUCIR21 team participated in both the offline and online subtasks of the NTCIR-16 Unbiased Learning to Rank Evaluation (ULTRE) task. This paper describes our approaches and reports the results in the ULTRE task. In the offline subtask, we tried four learning to rank models based on Mobile Click Model (MCM), as well as a revived Dual Learning Algorithm (DLA) model. In the online subtask, we revived a Pairwise Differentiable Gradient Descent (PDGD) run and two online DLA runs, we also tried an online DLA model based on MCM.
DOI: 10.20736/0002002311
Authors: Zechun Niu, Yurou Zhao, Feng Wang, Jiaxin Mao
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7143970004