Name
Title | ||
|---|---|---|
Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks |
Abstract | ||
|---|---|---|
•A new framework (COAT) is proposed for personalized knowledge-aware recommendation.•Collaborative and attentive GNNs are designed to jointly model the UI and KG graphs.•Novel attention mechanisms are designed to achieve personalization.•An efficient graph convolutional layer is employed to tackle the sparsity issue.•COAT outperforms 10 state-of-the-art recommendation methods on benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108628 | Pattern Recognition |
Keywords | DocType | Volume |
Recommender system,Graph convolutional network,Attention mechanism,Knowledge graph | Journal | 128 |
ISSN | Citations | PageRank |
0031-3203 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quanyu Dai | 1 | 28 | 5.28 |
| Xiao-Ming Wu | 2 | 0 | 0.34 |
| Lu Fan | 3 | 0 | 0.34 |
| Xiao-Ming Wu | 4 | 110 | 7.15 |
| Han Liu | 5 | 40 | 6.96 |
| Xiaotong Zhang | 6 | 159 | 27.16 |
| Dan Wang | 7 | 686 | 58.70 |
| Guli Lin | 8 | 3 | 1.41 |
| Keping Yang | 9 | 0 | 0.34 |