A5081332540- Recommender Systems and Techniques
- Advanced Graph Neural Networks
- Human Mobility and Location-Based Analysis
- Privacy-Preserving Technologies in Data
- Domain Adaptation and Few-Shot Learning
- Advanced Bandit Algorithms Research
- Multimodal Machine Learning Applications
Guangdong University of Technology
2022-2023
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled data, domain adaptation is important supervised graph tasks with limited samples. However, current methods are generally adopted from traditional tasks, and properties data not well utilized. For example, observed social networks different platforms controlled only by crowd or communities but also domain-specific policies background noise. Based these we first...
The recommendation system, relying on historical observational data to model the complex relationships among users and items, has achieved great success in real-world applications. Selection bias is one of most important issues existing data-based approaches, which actually caused by multiple types unobserved exposure strategies (e.g., promotions holiday effects). Though various methods have been proposed address this problem, they are mainly implicit debiasing techniques but not explicitly...
Sequential recommendation aims to choose the most suitable items for a user at specific timestamp given historical behaviors. Existing methods usually model behavior sequence based on transition-based such as Markov chain. However, these also implicitly assume that users are independent of each other without considering influence between users. In fact, this plays an important role in since is easily affected by others. Therefore, it desirable aggregate both behaviors and users, which...
The recommendation system, relying on historical observational data to model the complex relationships among users and items, has achieved great success in real-world applications. Selection bias is one of most important issues existing based approaches, which actually caused by multiple types unobserved exposure strategies (e.g. promotions holiday effects). Though various methods have been proposed address this problem, they are mainly implicit debiasing techniques but not explicitly...
Sequential recommendation aims to choose the most suitable items for a user at specific timestamp given historical behaviors. Existing methods usually model behavior sequence based on transition-based like Markov Chain. However, these also implicitly assume that users are independent of each other without considering influence between users. In fact, this plays an important role in since is easily affected by others. Therefore, it desirable aggregate both behaviors and users, which evolved...