A5075902723- Pancreatic and Hepatic Oncology Research
- Advanced Clustering Algorithms Research
- Context-Aware Activity Recognition Systems
- Advanced Causal Inference Techniques
- Diabetes Treatment and Management
- Human Mobility and Location-Based Analysis
- Data Mining Algorithms and Applications
- Epigenetics and DNA Methylation
- Recommender Systems and Techniques
- Face and Expression Recognition
Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage sequential pattern user actions, i.e. $impression\rightarrow click \rightarrow conversion$ to address issue. However, they still fail ensure unbiasedness CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from following...
Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice. In light of this, graph neural networks (GNNs) based models have recently been emerging as breakthroughs for this due to their ability learn global preferences and alleviate cold-start challenges. Nevertheless, most existing methods merely focus relations between POIs, neglecting higher-order information including trajectories...
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising. Treatment effect estimation, a fundamental problem causal inference, been extensively studied statistics for decades. However, traditional treatment estimation methods may not well handle large-scale high-dimensional heterogeneous data. In recent years, an emerging research direction attracted increasing attention the broad artificial intelligence...
Simpson's paradox is a well-known statistical phenomenon that has captured the attention of statisticians, mathematicians, and philosophers for more than century. The often confuses people when it appears in data, ignoring may lead to incorrect decisions. Recent studies have found many examples social data proposed few methods detect automatically. However, these suffer from limitations, such as being only suitable categorical variables or one specific paradox. To address problems, we...