Shijun Liu
Biographic Data
| ID | 8146955 |
|---|---|
| NAME | Shijun Liu |
| GIVEN NAMES | Shijun |
| FAMILY NAME | Liu |
| SIGNATURE | LIU S |
| AFFILIATIONS | Shandong University |
| ORCID | 0000-0002-3617-1210 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve
Nowadays, many dynamic recommendations still suffer from the insufficiency of finding user online interest evolving patterns because of those complicated interactions. In general, each interaction is usually impacted by multiple underlying reasons, which needs us to open the “box” of each interaction instance instead of simply treating them as a pair-wise link. Besides, different users usually perform differently for their long-term and short-ter…
Nus
Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new undersampling scheme, called a clustering-based noisy-sample-removed undersampling scheme (NUS) for i…
Enhancing Temporal Knowledge Graph Alignment in News Domain With Box Embedding
In many fields, such as social networks and recommendation systems with high time requirements, fake news and false information are often released in real time, impacting on people’s daily life. Entity alignment (EA) in temporal knowledge graph (TKG) can fuse the information contained in entities by finding equivalent entities, thus helping to determine the regular pattern of disinformation under time change. The existing methods either ignore th…
Lihan
Real-world heterogeneous information networks (HINs) are modeled as heterogeneous graphs, in which features and structures are often incomplete. Existing models employ manual imputation or dynamic adjustment to populate the incomplete data. However, there are some limitations in incomplete heterogeneous graph representation learning: 1) using populated data may lose content and high-level interaction information of HINs, even lead to negative imp…
The chemistry and pharmacology of Ligularia przewalskii
No prominent works on this page.
The chemistry and pharmacology of Ligularia przewalskii
Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve
Nowadays, many dynamic recommendations still suffer from the insufficiency of finding user online interest evolving patterns because of those complicated interactions. In general, each interaction is usually impacted by multiple underlying reasons, which needs us to open the “box” of each interaction instance instead of simply treating them as a pair-wise link. Besides, different users usually perform differently for their long-term and short-ter…
Nus
Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new undersampling scheme, called a clustering-based noisy-sample-removed undersampling scheme (NUS) for i…
Enhancing Temporal Knowledge Graph Alignment in News Domain With Box Embedding
In many fields, such as social networks and recommendation systems with high time requirements, fake news and false information are often released in real time, impacting on people’s daily life. Entity alignment (EA) in temporal knowledge graph (TKG) can fuse the information contained in entities by finding equivalent entities, thus helping to determine the regular pattern of disinformation under time change. The existing methods either ignore th…
Lihan
Real-world heterogeneous information networks (HINs) are modeled as heterogeneous graphs, in which features and structures are often incomplete. Existing models employ manual imputation or dynamic adjustment to populate the incomplete data. However, there are some limitations in incomplete heterogeneous graph representation learning: 1) using populated data may lose content and high-level interaction information of HINs, even lead to negative imp…
Computer Science (4 works) · Advanced Graph Neural Networks (3 works) · Artificial Intelligence (3 works) · Complex Network Analysis Techniques (3 works) · Theoretical Computer Science (3 works) · Data mining (2 works) · Embedding (2 works) · Engineering (2 works) · Graph (2 works) · Mathematics (2 works)