Weilong Chen

ORCID: 0000-0003-2202-601X
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About
Contact & Profiles
Research Areas
  • Topic Modeling
  • Sentiment Analysis and Opinion Mining
  • Luminescence Properties of Advanced Materials
  • Domain Adaptation and Few-Shot Learning
  • Advanced Text Analysis Techniques
  • Advanced Computational Techniques and Applications
  • Particle Detector Development and Performance
  • Particle accelerators and beam dynamics
  • Lanthanide and Transition Metal Complexes
  • Maritime Navigation and Safety
  • Particle Accelerators and Free-Electron Lasers
  • Human Mobility and Location-Based Analysis
  • Advanced Computing and Algorithms
  • Recommender Systems and Techniques
  • Glass properties and applications
  • Multimodal Machine Learning Applications

University of Electronic Science and Technology of China
2023-2024

Chinese Academy of Sciences
2024

Institute of Modern Physics
2024

Jinan University
2016

Liaocheng University
2012

Social media popularity prediction aims to predict future interaction or attractiveness of new posts. However, in most existing works, there is a notable deficiency the effective treatment numerical features. Despite their significant potential provide ample information, these features are often inadequately processed, leading insufficiency information acquirement. In this paper, we introduce method, named Double-Fine-Tuning Multi-Objective Vision-and-Language Transformer (DFT-MOVLT). To...

10.1145/3581783.3612845 article EN 2023-10-26

Recommendation systems, widely adopted in social networks, personalize user experiences through advanced technologies such as Reinforcement Learning (RL), known for producing high-performance, list- wise recommendations. However, RL-based recommendation methods exhibit biases, specifically: 1) Online bias, which stems from a complex real-world <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online policy</i> composed of various rules and...

10.1109/tkde.2024.3402649 article EN IEEE Transactions on Knowledge and Data Engineering 2024-05-20

Underwater communication is essential for environmental monitoring, marine biology research, and underwater exploration. Traditional faces limitations like low bandwidth, high latency, susceptibility to noise, while semantic (SC) offers a promising solution by focusing on the exchange of semantics rather than symbols or bits. However, SC encounters challenges in environments, including information mismatch difficulties accurately identifying transmitting critical that aligns with diverse...

10.48550/arxiv.2408.12616 preprint EN arXiv (Cornell University) 2024-08-08
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