Name
Abstract | ||
|---|---|---|
As an effective deep representation learning technique for graph data, graph convolutional network (GCN) has recently been widely applied to obtain better embedding of vertex. The existing studies have successfully explored user-item interaction via GCN for recommendation task and proved its effectiveness. We argue that a significant limitation of these methods is that social relation, which has been proven to impose positive effects for recommendation in many loss optimization models, has received relatively little scrutiny in GCN. Thus, the resultant embeddings are insufficient to model the potential social propagation effect. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.03.076 | Neurocomputing |
Keywords | DocType | Volume |
Social relation,Recommender system,Two-level attentional mechanism,Graph convolutional network | Journal | 449 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanbin Jiang | 1 | 2 | 2.43 |
| Huifang Ma | 2 | 290 | 29.69 |
| Yuhang Liu | 3 | 1 | 2.05 |
| Zhixin Li | 4 | 12 | 19.62 |
| Liang Chang | 5 | 23 | 14.22 |