Changlong Fu
Biographic Data
| ID | 8313337 |
|---|---|
| NAME | Changlong Fu |
| GIVEN NAMES | Changlong |
| FAMILY NAME | Fu |
| SIGNATURE | FU C |
| AFFILIATIONS | Yunnan University |
| ORCID | 0000-0001-8508-9830 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
GILMRec: Graph Invariant Learning for Multimodal Recommendation
Multimodal recommendation is a crucial technology on social media platforms. It is widely applied in scenarios such as product recommendation and advertising delivery. However, existing multimodal recommendation approaches often overlook invariant semantic features that persist across modalities, leading to decreased robustness and generalization. To address this limitation, we propose GILMRec , a novel g raph i nvariant l earning-based m ultimod…
Indirect Interactions Discovering and True Negative Sampling for Multimodal Recommendation
Multimodal recommendation has become a key technology for social media platforms. It is widely used in content recommendation, user preference analysis, advertisement placement, etc. Existing recommendation methods mainly focus on learning multimodal embeddings from direct interactions between users and items, ignoring indirect interactions among users-to-users and items-to-items. This limits the further exploration of potential interests between…
No prominent works on this page.
Indirect Interactions Discovering and True Negative Sampling for Multimodal Recommendation
Multimodal recommendation has become a key technology for social media platforms. It is widely used in content recommendation, user preference analysis, advertisement placement, etc. Existing recommendation methods mainly focus on learning multimodal embeddings from direct interactions between users and items, ignoring indirect interactions among users-to-users and items-to-items. This limits the further exploration of potential interests between…
GILMRec: Graph Invariant Learning for Multimodal Recommendation
Multimodal recommendation is a crucial technology on social media platforms. It is widely applied in scenarios such as product recommendation and advertising delivery. However, existing multimodal recommendation approaches often overlook invariant semantic features that persist across modalities, leading to decreased robustness and generalization. To address this limitation, we propose GILMRec , a novel g raph i nvariant l earning-based m ultimod…
Advanced Graph Neural Networks (1 works) · Advanced Text Analysis Techniques (1 works) · Artificial Intelligence (1 works) · Computer Science (1 works) · Discriminative model (1 works) · Feature learning (1 works) · Machine learning (1 works) · Machine Learning in Healthcare (1 works) · Modalities (1 works) · Recommender system (1 works)