Mr. Haoyan Fu | Recommendation System | Best Researcher Award

Published on by Network Awards

Mr. Haoyan Fu | Recommendation System | Best Researcher Award

Mr. Haoyan Fu, Beijing Institute of Technology, China

Mr. Haoyan Fu is currently pursuing a Master of Artificial Intelligence at Beijing Institute of Technology, expecting to graduate in June 2025 with a GPA of 3.58/4. He earned his Bachelor’s degree in Software Engineering from the same institution in June 2022. His research experience includes developing a novel time-guided diffusion model for time-aware sequential recommendation and creating a dynamic hypergraph learning paradigm to improve session-based recommendation frameworks. His innovative work showcases his commitment to advancing artificial intelligence technologies.

Professional Profile:

Orcid

Suitability for the Award:

Mr. Haoyan Fu stands out as an exceptional candidate for the Best Researcher Award due to his focus on cutting-edge methodologies in AI, specifically in the context of recommendation systems. His ability to innovate within the realm of Graph Neural Networks and Hypergraph Learning, combined with his strong academic background and published work in high-impact journals, makes him a worthy contender. The depth of his research aligns with industry needs for more accurate and scalable recommendation solutions, and his contributions to the field of AI-driven recommendations are likely to have long-term implications for both academia and industry.

Educational Background:

Mr. Haoyan Fu is currently pursuing a Master of Artificial Intelligence at Beijing Institute of Technology, with an expected graduation date in June 2025, maintaining a GPA of 3.58/4. He previously earned his Bachelor’s degree in Software Engineering from the same institution, graduating in June 2022 with a GPA of 3.56/4.

Technical Skills:

Haoyan is proficient in multiple programming languages, including Java, C++, Python, C, and SQL. He is skilled in utilizing advanced tools and frameworks such as TensorFlow, PyTorch, Scikit-Learn, NumPy, Pandas, Jupyter, and CUDA.

Research Experience:

  • Time-Aware Sequential Recommendation (Mar 2024 – Aug 2024) ⏳
    Haoyan proposed a novel time-guided diffusion model to address temporal sparsity in sequential data, developing a generalized Graph Neural ODE for aligning dynamic user and item representations over time.
  • Dynamic Hypergraph Learning for Session-based Recommendation (Aug 2023 – Feb 2024) 🔄
    He created a dynamic learning paradigm that transitioned from “graph” to “hypergraph,” integrating key relationships into session-based recommendation frameworks to enhance static graph information.

Publication Top Notes:

  • Title: Fusing Temporal and Semantic Dependencies for Session-Based Recommendation
    • Publication Year: 2025
  • Title: Diversified Graph Recommendation with Contrastive Learning
    • Publication Year: 2024
  • Title: Towards Relevance and Diversity in Crowdsourcing Worker Recruitment with Insufficient Information
    • Publication Year: 2024

 

 

 

 

Mr. Muhamamd Arslan Rauf | Recommendation system | Best Researcher Award

Published on by Network Awards

Mr. Muhamamd Arslan Rauf | Recommendation system | Best Researcher Award

Mr. Muhamamd Arslan Rauf, University of Electronic Science and Technology of China, China  

👨‍💼 Mr. Muhammad Arslan Rauf is a Ph.D. scholar specializing in Recommendation Systems, Zero-Shot Learning, and Deep Learning. With an extensive educational background including a Master’s degree in Computer Science, he brings deep analytical skills and research expertise to the table. Arslan has experience in both research and teaching, having served as a lecturer at Riphah International University. He is passionate about driving technological innovation in computer science and fostering a culture of continuous learning and innovation. Arslan’s certifications include courses in AWS Machine Learning and advanced statistical methods in Python.

🏫 Education and Training:

  • Doctorate in Software Engineering (PhD)
    • University of Electronic Science and Technology of China
    • Thesis: Zero-shot learning for Cold-strat Recommendation
  • Master of Science in Computer Science
    • National Textile University, Faisalabad, Pakistan
    • Final grade: CGPA 3.03/4.0, 71%
    • Thesis: Extraction of Strong and Weak regions of Cricket Batsmen through Text-commentary Analysis
    • Major: Machine Learning, NLP, Computer Vision

🏢 Work Experience:

  • Lecturer (Computer Science)
    • Riphah International University, Faisalabad, Pakistan
    • [24/09/2019 – 31/03/2021]

📜 Certification:

  • Getting Started with AWS Machine Learning – Coursera
  • Intro to Data and Data Science – 365 Data Science
  • Advanced Statistical methods in python – 365 Data Science

🔍 Research Interests:

Mr. Muhammad Arslan Rauf is a passionate Ph.D. scholar with a focus on cutting-edge research in Deep Learning and Recommendation Systems. His expertise also extends to Machine Learning and Zero-shot Learning. With a robust educational background and experience in both research and teaching, Arslan is committed to driving technological innovation and fostering a culture of continuous learning and innovation in computer science.

Publications Top Notes  :

  1. Fabric weave pattern recognition and classification by machine learning
    • Published in 2022 in the 2nd International Conference of Smart Systems and Emerging Technologies.
    • Cited by 2 articles.
  2. An Efficient Ensemble approach for Fake Reviews Detection
    • Published in 2023 in the 3rd International Conference on Artificial Intelligence (ICAI).
    • Cited by 1 article.
  3. Content-Based Venue Recommender Approach for Publication
    • Published in 2021 in the International Conference on Engineering Software for Modern Challenges.
    • Cited by 1 article.
  4. Extraction of Strong and Weak Regions of Cricket Batsman through Text-Commentary Analysis
    • Published in 2020 in the IEEE 23rd International Multitopic Conference (INMIC).
    • Cited by 1 article.