42 citations · 104 across the 22 of their papers we have counts for
24 papers
AlphaPADI: Formulaic Alpha Discovery via Pool-Aware Hierarchical Discrete Diffusion
Yanzheng Jin, Pengyang Shao, Yunshan Ma +5
Formulaic alpha discovery seeks symbolic expressions that predict cross-sectional asset returns. In deployment, multiple formulas are combined into an alpha pool, where each formul…
AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation
Yajing Yang, Yunshan Ma, Kelvin J. L. Koa +1
We argue that financial report generation should operate at the analytical rather than structural level, composing content from data-derived insights rather than high-level topics…
FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answering
Yujuan Ding, Linyin Luo, Shijie Wang +5
Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transforme…
CFALR: Collaborative Filtering-Augmented Large Language Model for Personalized Fashion Outfit Recommendation
Yujuan Ding, Junrong Liao, Yunshan Ma +4
Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibil…
ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting
Haoxuan Li, He Chang, Yunshan Ma +4
Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing…
iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification
Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang +2
Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixe…
Reasoning on Time-Series for Financial Technical Analysis
Kelvin J. L. Koa, Jan Chen, Yunshan Ma +2
While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Tec…
SemCORE: A Semantic-Enhanced Generative Cross-Modal Retrieval Framework with MLLMs
Haoxuan Li, Yi Bin, Yunshan Ma +4
Cross-modal retrieval (CMR) is a fundamental task in multimedia research, focused on retrieving semantically relevant targets across different modalities. While traditional CMR met…
Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
Jiajie Su, Qiyong Zhong, Yunshan Ma +5
Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To fur…
Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training
Haokai Ma, Yunshan Ma, Ruobing Xie +5
Recent research efforts have investigated how to integrate Large Language Models (LLMs) into recommendation, capitalizing on their semantic comprehension and open-world knowledge f…
Extending Visual Dynamics for Video-to-Music Generation
Xiaohao Liu, Teng Tu, Yunshan Ma +1
Music profoundly enhances video production by improving quality, engagement, and emotional resonance, sparking growing interest in video-to-music generation. Despite recent advance…
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…
Context-aware Event Forecasting via Graph Disentanglement
Yunshan Ma, Chenchen Ye, Zijian Wu +3
Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspec…
Causal Disentangled Recommendation Against User Preference Shifts
Wenjie Wang, Xinyu Lin, Liuhui Wang +3
Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has s…
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation
Yujuan Ding, Yunshan Ma, Wai Keung Wong +1
Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. Th…
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
Yunshan Ma, Yujuan Ding, Xun Yang +5
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR…
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media
Yujuan Ding, Yunshan Ma, Lizi Liao +2
Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devote…
Rethinking Dialogue State Tracking with Reasoning
Lizi Liao, Yunshan Ma, Wenqiang Lei +1
Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a proble…