What question did this study set out to answer?

This research aims to evaluate the performance of various learning to rank models in the ULTRE task.

April 1, 2026Open Access

RUCIR21 at the NTCIR-16 ULTRE Task

Puntos clave

This research aims to evaluate the performance of various learning to rank models in the ULTRE task.
Participated in offline and online subtasks of the NTCIR-16 ULTRE task.
Tested four learning to rank models based on Mobile Click Model.
Revived Dual Learning Algorithm for model evaluation.
Implemented Pairwise Differentiable Gradient Descent in one run.
Examined online variations of DLA based on MCM.
Demonstrated varying effectiveness of different learning to rank models.
Offline approaches provided insights into model performance.
Revival of DLA showed potential for improved results in online scenarios.

Resumen

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.

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Cite This Study

Niu et al. (Tue,) studied this question.

synapsesocial.com/papers/69cd7ac55652765b073a8307 https://doi.org/https://doi.org/10.20736/0002002311

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