🔎 Research Interest
My research aims to understand how users engage with online content and to improve the services they experience. Specifically, I focus on: (1) mining large-scale real-world online and web data, such as social networks and e-commerce platforms, to uncover patterns in user behavior and interests; (2) modeling user preferences to personalize experiences, including recommendation; and (3) developing agentic systems that deliver more useful, user-centered services.
|
📚 Publications
|
|
Personalized Reward Modeling for Text-to-Image Generation
Jeongeun Lee, Ryang Heo, Dongha Lee
ECCV, 2026
Paper
/
Code
|
|
|
Will It Go Viral? Grounding Micro-Video Popularity Prediction on the Open Web
Ryang Heo, Dongha Lee
arXiv, 2026
Paper
/
Code
|
|
|
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths
Sangam Lee, Ryang Heo, SeongKu Kang, Susik Yoon, Jinyoung Yeo, Dongha Lee
ACL Findings, 2026
Paper
/
Code
|
|
|
AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping
{Sunghwan Kim, Ryang Heo}, Yongsik Seo, Jinyoung Yeo, Dongha Lee
WWW, 2026
Paper
/
Code
|
|
|
Can Large Language Models be Effective Online Opinion Miners?
Ryang Heo, Yongsik Seo, Junseong Lee, Dongha Lee
EMNLP Main, 2025
Paper
/
Code
|
|
|
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
Sangam Lee, Ryang Heo, SeongKu Kang, Dongha Lee
COLM, 2025
Paper
/
Code
|
|
|
Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis
{Yongsik Seo, Sungwon Song, Ryang Heo}, Jieyong Kim, Dongha Lee
EMNLP Findings, 2024
Paper
/
Code
|
|
|
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy
{Jieyong Kim, Ryang Heo}, Yongsik Seo, SeongKu Kang, Jinyoung Yeo, Dongha Lee
ACL Findings, 2024
Paper
/
Code
|
|