Comments (4)
from knowledge_graph_based_intent_network.
Sorry for the late reply. The experiments are conducted on a single Linux server with 512G RAM and 8 NVIDIA GeForce v100-32GB.
from knowledge_graph_based_intent_network.
Thank you for your reply.In the training log of Amazon-Book dataset, the training time of eachepoch is 800+ seconds, which is unacceptable. I used to use the whole last.FM Data sets (3 million interactions and 460000 + triplets) and also use Tesla V100 to train KGAT, and the average train time of each epoch is only 182 seconds. KGIN has no additional knowledge Graph representation learning(e.g. transR). Why does it spend so much time training?
from knowledge_graph_based_intent_network.
I think the most time-consuming procedure is calculating the equation (7)(10), which is implemented in the "KGIN" file (line 35-38). Here I use the torch_scatter package to aggregate the neighbor embedding, and it can be further optimized or replaced by another efficient implementation.
from knowledge_graph_based_intent_network.
Related Issues (20)
- 训练过程报错“name 'test_user_set' is not defined” HOT 9
- A question about equation7 HOT 3
- 关于KGAT等基线方法实验结果与KGAT原论文中的不同 HOT 2
- 代码问题 HOT 1
- How to get the result like Fig 5 in your paper? HOT 1
- How do I know the total distribution and score of R in P in Figure 5 HOT 5
- Questions for raw data to data HOT 2
- 模型处理大规模数据集时cuda OOM HOT 4
- latent_emb问题 HOT 1
- Lastfm数据集 HOT 2
- intent的embedding以及意图独立性时的参数等问题 HOT 6
- 您好,论文内容有一处不解,冒昧请教 HOT 1
- KGIN关于推荐的问题 HOT 4
- Questions about the latent_emb. HOT 3
- 可否提供rgcn在推荐上的的复现代码
- 公式7的实现
- 训练速度问题 HOT 3
- 关于utils/data_loader.py第69行注释的小疑问
- About the dataset Alibaba-iFashion
- Can you tell me about kgfinal.txt
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from knowledge_graph_based_intent_network.