Yu Hou

Ph.D. Course Student

50 Yonsei-Ro, Seodaemun-Gu
School of Mathematics and Computing
(Computational Science and Engineering)
Yonsei University
Seoul, Korea

Email: houyu@yonsei.ac.kr

Biography

I am currently pursuing my Ph.D. in the Department of Computational Science and Engineering (CSE) at Yonsei University. My academic journey began with a Master's degree in Applied Mathematics, which I received from Ocean University of China (OUC) in Qingdao in 2021. My research interests center on graph learning, data analysis, and recommender systems, reflecting my enthusiasm for applying mathematical and computational techniques to understand and solve complex real-world problems.

I have been exploring the integration of diffusion models with graph learning, aiming to tackle real-world problems, such as recommender systems. Recently, my research has focused on cutting-edge techniques, recommender systems, and LLM-based recommender systems, leveraging LLMs to better capture semantic information and enhance recommendation quality.

Selected Publications

	Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation Yu Hou, Won-Yong Shin. The 40th Annual AAAI Conference on Artificial Intelligence (AAAI), Singapore, 2026 [paper]
	TRACE: Tourism Recommendation with Accountable Citation Evidence Zixu Zhao, Sijin Wang, Yu Hou, Yuanyuan Xu, Yufan Sheng, Xike Xie, Wenjie Zhang, Won-Yong Shin, Xin Cao The 40th Annual Conference on Neural Information Processing Systems (NeurIPS), Sydney, Australia, 2026 [paper]
	Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity Yu Hou, Jin-Duk Park, Won-Yong Shin. The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Washington DC, USA, 2024. [paper]
	Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation Yu Hou, Nathaniel Kang, Pengkai Wang, Hua Li. arXiv [paper]
	Remembering What Lasts, Adapting to What Changes: Better User Understanding for LLM-Enhanced Sequential Recommendation Yu Hou. arXiv [paper]
	N/A (double-blind peer review) Yu Hou, Yi Xu, Zixu Zhao, Xin Cao, Won-Yong Shin. Submitted to the 32nd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Jeju, Korea, 2026
	N/A (double-blind peer review) Yi Xu, Yu Hou, Won-Yong Shin, Zhiqiang Xu, Xin Cao Submitted to the 32nd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Jeju, Korea, 2026
	Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization Yu Hou, Hua Li, Ha Young Kim, Won-Yong Shin. Submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE)
	A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty Yu Hou, Cong Tran, Ming Li, Won-Yong Shin. IEEE Transactions on Network Science and Engineering (TNSE), 12(4): 3159-3176, 2025. (SCI) [paper]
	Robust Mesh Segmentation Using Feature-Aware Region Fusion Lulu Wu, Yu Hou, Junli Xu, Yong Zhao. Sensors, 23 (1): 416, 2023. (SCI) [paper]
	META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks Yu Hou, Cong Tran, Won-Yong Shin. The 31st ACM International Conference on Information and Knowledge Management (CIKM), Atlanta, Georgia, USA, 2022. [paper]
	3D Mesh Segmentation via L0-constrained Random Walks Yu Hou, Yong Zhao, Xin Shan. Multimedia Tools and Applications, 80 (16), 2021. (SCI) [paper]
	A Robust Segmentation Algorithm for 3D Complex Meshes Yu Hou, Yong Zhao. The 2020 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation (CDMMS), Busan, Republic of Korea, 2020. (EI)
	Application of Deep Learning Method in High Resolution Processing of Significant Wave Height Xiaowen Zhu, Yu Hou, Yuhai Liu, Kejian Wu. Periodical of Ocean University of China, 50 (11), 2020.

Projects

Reliable Agentic AI Platform for Financial Research
An agentic financial research platform for stocks and cryptocurrencies that interprets requests, selects relevant tools, and produces evidence-backed insights from market data, news, financial statements, and historical memory. On a 14-case labelled benchmark, it achieved 92.9% task-classification accuracy, 96.5% tool-selection F1, and 100% coverage of required evidence types, with zero invalid evidence-ID references detected. Structured model outputs, deterministic Python calculations, and verification checks support reliability, while decision traces make tool choices, evidence sources, and limitations inspectable.

Honors & Awards

K-STAR Visa Track Outstanding Talent, Yonsei University Graduate School, 2026.

Graduate Innovation Excellence Paper Award, 2025.

BK21 Plus Scholarship, 2025.

BK21 Plus Scholarship, 2024.

BK21 Plus Scholarship, 2023.

International Student Scholarship, Yonsei University, Fall, 2023.

International Student Scholarship, Yonsei University, Fall, 2022.

International Student Scholarship, Yonsei University, Spring, 2022.

International Student Scholarship, Yonsei University, Fall, 2021.

Postgraduate Academic Innovation Scholarship, Ocean University of China, 2021.

Excellent Graduate Student, Ocean University of China, 2021.

Cultural, Sports and Social Activities Scholarship, Ocean University of China, 2020.

"Sharing Cup" Innovation Competition of Science and Technology Resources Sharing Service for College Student, Excellence Award, 2020.

Science and Technology Competition Scholarship, Ocean University of China, 2019.

National Post-Graduate Mathematical Contest in Modeling, Third Prize Award, 2019.

Activities

July. 2026, Selected as a K-STAR Visa Track Outstanding Talent and received the university president’s recommendation for the F-2-7S residence visa track, Yonsei University

Jan. 2026 - Feb. 2026, Visiting Scholar at the University of New South Wales (UNSW).

Jan. 2026, Poster Presentation at the 40th AAAI Conference on Artificial Intelligence (AAAI2026).

July 2024, Oral Presentation at the 47th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2024).

Invited reviewer in IEEE Transactions on Knowledge and Data Engineering (TKDE).

Invited presentation: Yu Hou, Cong Tran, and Won-Yong Shin, "Community Detection Meets Exploratory Learning," presented at the A3 Foresight Program – AI-Based Future IoT Technologies and Services Workshop, Tokyo, Japan, December 2022.

Invited presentation: Yu Hou, Cong Tran, and Won-Yong Shin, “Overlapping community detection via network exploration,” presented at the Convergence Technology Conference (CTCon), Ho Chi Minh City, Vietnam, December 2022.

Invited reviewer in WSDM 2023 and WWW 2023.

Education

Visiting Research Scholar

Jan. 2026 - Feb. 2026

University of New South Wales (UNSW), Sydney, Australia

Research focus: LLM-based Recommender Systems
Advisor: Prof. Xin Cao

Ph.D. course student in Computational Science and Engineering

Sept. 2021 - Present

Yonsei University, Seoul, Korea

Research interests：Graph Learning, Data Analysis, Recommender System
Advisor: Prof. Won-Yong Shin

Master in Applied Mathematics

Sept. 2018 - Jun. 2021

Ocean University of China, Qingdao, Shandong, China

Research interests：Computer Graphics, Data Analysis, Deep Learning
Advisor: Prof. Yong Zhao

Bachelor in Mathematics and Applied Mathematics

Sept. 2012 - Jun. 2016

Ocean University of China, Qingdao, Shandong, China

Relevant Teaching

Teaching assistant: CSE9983 Fundamentals and Emerging Trends in Graph Learning 4, Spring, 2026.

Teaching assistant: CSE5023 Fundamentals of Neural Networks and Reinforcement Learning, Spring, 2024.

Teaching assistant: CSE9981 Fundamentals and Emerging Trends in Graph Learning 2, Spring, 2023.

Experience

Sugon, AI Ocean Research Group, Qingdao, Shandong, China

Mar. 2019 - Jan. 2020

Algorithm Research Intern
Topic: Deep Learning for Biodensity Estimation

JOYO, Research and Development Department, Shenzhen, Guangdong, China

Apr. 2018 - Aug. 2018

Algorithm Engineer
Topic: Signal Processing Algorithm Design