What question did this study set out to answer?

This research aims to explore effective methods in session search tasks using advanced ranking models.

April 1, 2026Open Access

RUCIR at the NTCIR-16 Session Search (SS) Task

Key Points

This research aims to explore effective methods in session search tasks using advanced ranking models.
Utilized the COCA ranking model based on BERT and contrastive learning.
Applied the BM25 algorithm for enhanced ranking performance.
Incorporated usefulness labels to improve accuracy of results.
The best run outperformed all other participants across all official metrics.
Achieved top performance in both subtasks of the competition.

Abstract

This paper presents the participation of RUCIR in the NTCIR-16 Session Search Task. We will discuss the approach we use to solve the problem and the experimental results. We use the state-of-the-art session search ranking model COCA which is based on BERT and contrastive learning. In addition, we use the BM25 algorithm and usefulness labels to make our ranking results more accurate. The official results show that our best run outperforms all other participants' runs in terms of all official metrics in both subtasks.

Read Full Paperexternally

Ask AI

Helpful

Bookmark

View Full Paper