18 citations · 47 across the 27 of their papers we have counts for
5 papers · 1 filter
FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering
Jijun Chi, Zhenghan Tai, Hanwei Wu +21
Financial question answering over U.S. Securities and Exchange Commission (SEC) filings requires retrieving and synthesizing heterogeneous evidence dispersed across long, standardi…
VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, Hanwei Wu, Qingchen Hu +24
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from…
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the abi…
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. A…
Memory Augmented Graph Neural Networks for Sequential Recommendation
Chen Ma, Liheng Ma, Yingxue Zhang +3
The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may de…