Name
Abstract | ||
|---|---|---|
Graph Neural Networks (GNNs) has been widely used to address the sparsity and cold start problems in recommendation system. By propagating embeddings from multi-hop neighbor nodes among the interaction graph and update target user and item embeddings, GNNs-based methods can achieve better recommendation performance. But those methods directly concatenate the output of each layer and ignore the dif... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SWC50871.2021.00015 | 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI) |
Keywords | DocType | ISBN |
Recommendation System,Multi-order Convolution,Graph Neural Networks,Collaborative Filtering | Conference | 978-1-6654-1236-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingshuai Kou | 1 | 0 | 0.34 |
| Neng Gao | 2 | 1 | 4.07 |
| Jia Peng | 3 | 0 | 0.34 |
| Jiong Wang | 4 | 49 | 12.67 |
| Min Li | 5 | 0 | 1.35 |
| Yiwei Shan | 6 | 0 | 0.34 |