42 citations · 104 across the 22 of their papers we have counts for
7 papers · 1 filter
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling
Shuo Xu, Haokai Ma, Yunshan Ma +4
Product bundling aims to organize a set of thematically related items into a combined bundle for shipment facilitation and item promotion. To increase the exposure of fresh or over…
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes
Kelvin J. L. Koa, Yunshan Ma, Yi Xu +3
Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical inform…
Addressing Graph Heterogeneity and Heterophily from A Spectral Perspective
Kangkang Lu, Yanhua Yu, Zhiyong Huang +6
Graph neural networks (GNNs) have demonstrated excellent performance in semi-supervised node classification tasks. Despite this, two primary challenges persist: heterogeneity and h…
MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models
Haoxuan Li, Zhengmao Yang, Yunshan Ma +3
We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of ut…
FashionReGen: LLM-Empowered Fashion Report Generation
Yujuan Ding, Yunshan Ma, Wenqi Fan +3
Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generatin…
Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
Kelvin J. L. Koa, Yunshan Ma, Ritchie Ng +1
Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights…
Smart Fitting Room: A One-stop Framework for Matching-aware Virtual Try-on
Mingzhe Yu, Yunshan Ma, Lei Wu +4
The development of virtual try-on has revolutionized online shopping by allowing customers to visualize themselves in various fashion items, thus extending the in-store try-on expe…