Postdoctoral Research Fellow at
NExT++ Research Center and National Cybersecurity R&D Lab
School of Computing
National University of Singapore
Email: yunshan dot ma at u dot nus dot edu dot com or mysbupt at gmail.com
• Google Scholar Page • GitHub Page
Biography
I am a Postdoctoral Research Fellow in National University of Singapore, where I am a member of NExT++ Research Center, supervised by Prof. Chua Tat-Seng. And I am also under National Cybersecurity R&D Lab and work with Prof. Chang Ee-Chien and Prof. Liang Zhenkai. My research focuses on the emerging and practical problem of multimodal event forecasting, which aims to understand multimodal information and make forecasting in various domains, such as international politics, macro economy, finance, cyber threats, fashion trend, etc. I am also working on computational fashion, including personalized mix-and-match, fashion outfit generation, fashion report generation, etc. In addition, I study a specialized branch of recommender system, i.e., bundle recommendation, including multimodal bundle construction and recommendation. In summary, I am interested in investigating computational (e.g., data mining, information retrieval, and multimedia) approaches to solve realworld problems. I am enthusiatic in discovering, formulating, and solving new and crucial problems, which have been not or rarely touched by the current academic and industrial community.
Prospective Ph.D., Master, and Undergraduate Students
I am looking for highly motivated students (PhD, master, undergraduate students) to work together on multimodal event forecating, computational fashion, and recommender system. Please feel free to send me your CV and transcripts, if you have interest. We are also actively looking for opportunities in research, partnership and commercialization in exciting data science projects.
News
Fine-tuning Multimodal Large Language Models for Product Bundling. [pdf] [code coming soon]
AttacKG+: Boosting Attack Graph Construction with Large Language Models. [pdf]
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling. [pdf] [code & dataset]
MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models. [pdf] [code & dataset]
ACL'24: Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding. [pdf] [code & dataset]
KDD'24: LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation. [pdf] [code & dataset]
KSEM'24: EE-LCE: An Event Extraction Framework Based on LLM-Generated CoT Explanation. [pdf] [code]
Smart Fitting Room: A One-stop Framework for Matching-aware Virtual Try-On. [pdf] [code & dataset]
WWW'24 Demo: FashionReGen: LLM-Empowered Fashion Report Generation. [pdf]
SIGIR'24: Diffusion Models for Generative Outfit Recommendation. [pdf] [code & dataset]
Filter-based Stance Network for Rumor Verification. [pdf]
WWW'24: Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models. [pdf] [code & dataset]
TOIS: MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation. [pdf] [code & dataset]
Enhancing Item-level Bundle Representation for Bundle Recommendation. [pdf] [code & dataset]
WSDM'24: Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction. [pdf] [code & dataset]
TKDE: Rule-guided Counterfactual Explainable Recommendation. [pdf]
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction. [pdf] [code & dataset]
RA-L: A Learning-Based Approach for Estimating Inertial Properties of Unknown Objects from Encoder Discrepancies. [pdf] [code & dataset]
IPM: Personalized Fashion Outfit Generation with User Coordination Preference Learning. [pdf]
Context-aware Event Forecasting via Graph Disentanglement. [pdf] [code & dataset]
FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs. [pdf]
Strategy-aware Bundle Recommender System. [pdf] [code & dataset]
Causal Disentangled Recommendation Against User Preference Shifts. [pdf] [code & dataset]
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation. [pdf] [code & dataset]
Professional Services
- I will be a volunteers co-chair in The Web Conf 2024.
- I chair the session of Recommendations and Ads of SIRIP in SIGIR 2023.
- I serve as an area co-chair of Information Retrieval, Text Classification, and Question Answering in CCL 2023.
- KDD, WWW, SIGIR, IJCAI, ACMMM, WSDM, AAAI, etc.
- TKDE, TOIS, TMM, ToMM, TWeb, TIST, etc.
Honors and Awards
Background
Supervisor: Prof Tat-Seng Chua.
Supervisor: Prof Tat-Seng Chua.