Ying Ding

Interactive Graph Visualization and Teaming Recommendation in an Interdisciplinary Project's Talent Knowledge Graph

2025 · Proceedings of the Association for Information Science and Technology

Interactive visualization of large scholarly knowledge graphs combined with LLM reasoning shows promise but remains under‐explored. We address this gap by developing an interactive visualization system for the Cell Map for AI Talent Knowledge Graph …

Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning

2025 · arXiv (Cornell University)

Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narratives offer rich insights into patient and caregiver experiences, manual thematic analysis (TA) remains labor-intensive and unscalable. We propose …

Towards Opinion Summarization from Online Forums

2015 · Recent Advances in Natural Language Processing

Summarizing opinions expressed in online forums can potentially benefit many people. However, special characteristics of this problem may require changes to standard text summarization techniques. In this work, we present our initial attempt at extractive …

Improving Automated Bug Triaging with Specialized Topic Model

2016 · IEEE Transactions on Software Engineering

Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers …

Aspect-Aware Latent Factor Model

2018

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual …

A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

2018

Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems …