Demo Data from VLDB 2026 / reviewers in the wild / expert
Yuelyu Ji
dblp:342/8019
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0001-6389-5823ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › reranking
document re-ranking |
0.9 | 1 | 2025 | Reason-to-Rank: Distilling Direct and Comparative Reasoning from Large Language Models for Document Reranking · SIGIR 2025 |
Information retrieval › ranking › ranking model
interpretable ranking |
0.9 | 1 | 2025 | Reason-to-Rank: Distilling Direct and Comparative Reasoning from Large Language Models for Document Reranking · SIGIR 2025 |
Information retrieval
ranking |
0.9 | 1 | 2025 | Reason-to-Rank: Distilling Direct and Comparative Reasoning from Large Language Models for Document Reranking · SIGIR 2025 |
Methods — techniques the papers use, named apart from their topics
large language model prompting · 0.9knowledge distillation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Reason-to-Rank: Distilling Direct and Comparative Reasoning from Large Language Models for Document RerankingabstractReranking documents in information retrieval often relies on black-box models that improve effectiveness but lack explainability. We introduce Reason-to-Rank (R2R), a novel framework that separates direct relevance reasoning from comparison reasoning to provide both direct and comparitive explanations. We first prompt a large language model to produce comprehensive rationales and a ranking order; then we distill both the ranking decisions and textual explanations into a smaller, open-source student model. Our approach not only improves retrieval performance, as demonstrated in MSMARCO, BEIR, and BRIGHT, but also provides interpretable justifications for why one document outranks another. We report NDCG@5 (and NDCG@10) for direct comparisons with prior work, and show that the distilled student model achieves competitive results while significantly reducing computational overhead. By unifying direct and comparative reasoning in a single pipeline, R2R bridges the gap between transparency and effectiveness in modern reranking systems. Yuelyu Ji, Zhuochun Li, Daqing He |
SIGIR | 1 |
| 2023 | Mapping dementia caregivers' comments on social media with evidence-based care strategies for memory loss and confusionabstractDementia caregivers widely turn to social media for much needed information and support. Prior research on caregivers’ online information exchange has focused on the original questions or posts, without including peer comments in response to those original questions, which does not provide a complete picture of online information exchanges between caregivers and their peers. This paper provides a preliminary analysis of a subset of data from a larger project and suggests how peer comments might match with evidence-based care strategies for memory loss and confusion. A total of 954 peer comments on 114 Reddit posts for dementia memory loss and confusion were collected and mapped with 5 evidence-based strategies generated from 17 care strategies from 3 credible websites. Our results report how well peer comments on Reddit can map to existing evidence-based strategies, and provide preliminary evidence supporting the necessity of providing tailored information based on the patient’s characteristics, stages of disease, and the progression level of memory loss and confusion. Ning Zou, Yuelyu Ji, Bo Xie 0001, Daqing He, Zhimeng Luo |
CHIIR | 2 |