Chang-Dong Wang
Biographic Data
| ID | 10054144 |
|---|---|
| NAME | Chang-Dong Wang |
| GIVEN NAMES | Chang-Dong |
| FAMILY NAME | Wang |
| SIGNATURE | WANG C |
| AFFILIATIONS | Sun Yat-sen University |
| ORCID | 0000-0001-5972-559X |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
MLSA4Rec
Super–Item Interaction With Contrastive Learning for Structure-Level Cross-Domain Recommendation
Cross-domain recommendation aims to leverage knowledge from multiple domains to mitigate issues of data sparsity and cold–start problems. While traditional cross-domain settings often involve semantic domains ( e.g. , movies versus books), recent research has expanded this notion to include structure-level domains that reflect different types of interaction graphs. To this end, we propose Super–Item Interaction with Contrastive Learning for Struc…
Knowledge-Aware Graph Prompt Tuning for Cross-Domain Recommendation
Cross-domain recommendation (CDR) has received attention to solve the cold-start and data sparsity problems. Existing methods mainly focus on the information about overlapping users or items, neglecting to effectively and efficiently utilize the information about nonoverlapping users or items in the source domain. Currently, graph prompt learning is proposed to bridge the gap between the pretrained tasks and downstream tasks, which can fully use …
Collaborative Meta-Path Modeling for Explainable Recommendation
Although recommender systems have achieved considerable success, sometimes it is difficult to convince users due to the failure to explain the recommendation results. For this reason, explainable recommender systems have drawn a lot of attention in recent years. Among explainable recommendation models, the meta-path-based model plays a significant role because it can reason over the path connecting a user–item pair to achieve explainability. Howe…
Community Enhanced Knowledge Graph for Recommendation
Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with th…
Multi-Information Preprocessing Event Extraction With BiLSTM-CRF Attention for Academic Knowledge Graph Construction
Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, …
No prominent works on this page.
Multi-Information Preprocessing Event Extraction With BiLSTM-CRF Attention for Academic Knowledge Graph Construction
Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, …
Collaborative Meta-Path Modeling for Explainable Recommendation
Although recommender systems have achieved considerable success, sometimes it is difficult to convince users due to the failure to explain the recommendation results. For this reason, explainable recommender systems have drawn a lot of attention in recent years. Among explainable recommendation models, the meta-path-based model plays a significant role because it can reason over the path connecting a user–item pair to achieve explainability. Howe…
Community Enhanced Knowledge Graph for Recommendation
Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with th…
Knowledge-Aware Graph Prompt Tuning for Cross-Domain Recommendation
Cross-domain recommendation (CDR) has received attention to solve the cold-start and data sparsity problems. Existing methods mainly focus on the information about overlapping users or items, neglecting to effectively and efficiently utilize the information about nonoverlapping users or items in the source domain. Currently, graph prompt learning is proposed to bridge the gap between the pretrained tasks and downstream tasks, which can fully use …
MLSA4Rec
Super–Item Interaction With Contrastive Learning for Structure-Level Cross-Domain Recommendation
Cross-domain recommendation aims to leverage knowledge from multiple domains to mitigate issues of data sparsity and cold–start problems. While traditional cross-domain settings often involve semantic domains ( e.g. , movies versus books), recent research has expanded this notion to include structure-level domains that reflect different types of interaction graphs. To this end, we propose Super–Item Interaction with Contrastive Learning for Struc…
Recommender Systems and Techniques (5 works) · Advanced Graph Neural Networks (4 works) · Artificial Intelligence (4 works) · Computer Science (4 works) · Graph (4 works) · Data science (3 works) · Knowledge graph (3 works) · Mathematics (3 works) · Recommender system (3 works) · Theoretical Computer Science (3 works)