VLDB 2026 Research / reviewers in the wild / expert
Pingping Chen 0004
dblp:88/10173-4
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0008-6979-401XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › document retrieval › domain-specific retrieval
financial information retrieval |
0.9 | 1 | 2025 | FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025 |
Information retrieval
retrieval-augmented generation |
0.9 | 1 | 2025 | FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025 |
Information retrieval › evaluation
benchmark |
0.3 | 1 | 2025 | FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025 |
Information retrieval
evaluation |
0.3 | 1 | 2025 | FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025 |
Information retrieval
trustworthy information retrieval |
0.3 | 1 | 2025 | FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI · SIGIR 2025 |
Methods — techniques the papers use, named apart from their topics
large language model · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AIabstractRecent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) and trustworthiness, privacy and security when applying financial IR and generative models. This workshop aims to deepen understanding, accelerate progress, and support the advancement of IR technology to enhance generative models to address financial challenges. Fengbin Zhu, Yunshan Ma 0002, Fuli Feng, Chao Wang 0049, Huan-Bo Luan, Guangnan Ye, Shuo Zhang 0006, Dhagash Mehta, Pingping Chen 0004, Bing Xiang, Tat-Seng Chua |
SIGIR | 9 |