1 citations · 3 across the 10 of their papers we have counts for
11 papers
Code-Interactive Visual Reasoning for Financial Candlestick Understanding
Chenxi Zhang, Youwei Pang, Xiaoqi Zhao +4
Financial candlestick understanding requires locating visual elements, aligning them across trading dates and panels, and combining observations through numerical and financial rea…
Think2Go: Generative Next POI Recommendation with LLM Reasoning
Zhuang Zhuang, Shanshan Feng, Hangwei Qian +4
Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next de…
Text-attributed Graph Condensation via Text Selection and Attribute Matching
Haowei Han, Yuxiang Wang, Guojia Wan +5
Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neural Network (GNN) and languag…
Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization
Hongliang Qiao, Shanshan Feng, Min Zhou +5
The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diff…
The Digital Ecosystem of Beliefs: does evolution favour AI over humans?
David M. Bossens, Shanshan Feng, Yew-Soon Ong
As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understa…
Prototype Optimization with Neural ODE for Few-Shot Learning
Baoquan Zhang, Shanshan Feng, Bingqi Shan +3
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a…
Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons
Jiao Liu, Zhu Sun, Shanshan Feng +2
In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functiona…
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
Lanting Fang, Yulian Yang, Yawei Zhang +3
Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems. However, most existing CTDG representation learning methods are tailored to in-d…
Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation
Shanshan Feng, Haoming Lyu, Caishun Chen +1
Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment. Existing studies rely on building recommendation model…
Virtual Co-Pilot: Multimodal Large Language Model-enabled Quick-access Procedures for Single Pilot Operations
Fan Li, Shanshan Feng, Yuqi Yan +2
Advancements in technology, pilot shortages, and cost pressures are driving a trend towards single-pilot and even remote operations in aviation. Considering the extensive workload…
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
Ziqi Yin, Shanshan Feng, Shang Liu +3
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that considers both spatial and…