Wenge Rong
Biographic Data
| ID | 3916892 |
|---|---|
| NAME | Wenge Rong |
| GIVEN NAMES | Wenge |
| FAMILY NAME | Rong |
| SIGNATURE | RONG W |
| AFFILIATIONS | Beihang University |
| ORCID | 0000-0002-4229-7215 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
KDG-Rec: Enhanced Dual-GNN Programming Exercise Recommendation via LLM-Powered Knowledge Annotation and Preference-Decoupling
The rapid expansion of online programming exercise platforms has brought abundant learning resources for programming education, but also presents challenges for personalized exercise recommendation due to missing or imprecise knowledge annotations and the sparsity of learner-exercise interaction records, which together lead to reduced accuracy and a lack of explainability in the recommendation results. Existing natural language processing and lar…
Wasserstein Dependent Graph Attention Network for Collaborative Filtering With Uncertainty
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertainty, they suffer from several limitations when introduced to graph-based recommender syste…
Causal Intervention for Fairness in Multibehavior Recommendation
Recommender systems usually learn user interests from various user behaviors, including clicks and postclick behaviors (e.g., like and favorite, which reflects the true interests of users). However, these behaviors inevitably exhibit popularity bias, leading to some unfairness issues: 1) for items with similar quality, more popular ones get more exposure; and 2) even worse the popular items with lower popularity might receive more exposure. Exist…
Multimodal Contrastive Transformer for Explainable Recommendation
Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the genera…
An Object Tuple Model for Understanding Pointer and Array in C Language
Contribution: In this study, an object tuple model has been proposed, and a quasi-experimental study on its usage in an introductory programming language course has been reported. This work can be adopted by all C language teachers and students in learning pointer and array-related concepts. Background: C language has been extensively employed in numerous universities as an introductory programming practice. However, the pointer and array have lo…
No prominent works on this page.
An Object Tuple Model for Understanding Pointer and Array in C Language
Contribution: In this study, an object tuple model has been proposed, and a quasi-experimental study on its usage in an introductory programming language course has been reported. This work can be adopted by all C language teachers and students in learning pointer and array-related concepts. Background: C language has been extensively employed in numerous universities as an introductory programming practice. However, the pointer and array have lo…
Causal Intervention for Fairness in Multibehavior Recommendation
Recommender systems usually learn user interests from various user behaviors, including clicks and postclick behaviors (e.g., like and favorite, which reflects the true interests of users). However, these behaviors inevitably exhibit popularity bias, leading to some unfairness issues: 1) for items with similar quality, more popular ones get more exposure; and 2) even worse the popular items with lower popularity might receive more exposure. Exist…
Multimodal Contrastive Transformer for Explainable Recommendation
Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the genera…
Wasserstein Dependent Graph Attention Network for Collaborative Filtering With Uncertainty
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertainty, they suffer from several limitations when introduced to graph-based recommender syste…
KDG-Rec: Enhanced Dual-GNN Programming Exercise Recommendation via LLM-Powered Knowledge Annotation and Preference-Decoupling
The rapid expansion of online programming exercise platforms has brought abundant learning resources for programming education, but also presents challenges for personalized exercise recommendation due to missing or imprecise knowledge annotations and the sparsity of learner-exercise interaction records, which together lead to reduced accuracy and a lack of explainability in the recommendation results. Existing natural language processing and lar…
Computer Science (4 works) · Artificial Intelligence (3 works) · Mathematics (2 works) · Psychology (2 works) · Recommender Systems and Techniques (2 works) · Advanced Graph Neural Networks (1 works) · Air Quality Monitoring and Forecasting (1 works) · Annotation (1 works) · Artificial Intelligence (1 works) · Behavioral Health and Interventions (1 works)