图卷积神经网络的推荐算法代码
时间: 2023-10-04 07:12:41 浏览: 42
以下是一个简单的图卷积神经网络推荐算法的代码示例,使用 PyTorch 框架实现:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
class GraphConv(nn.Module):
def __init__(self, in_features, out_features):
super(GraphConv, self).__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x, adj):
x = self.linear(x)
x = torch.spmm(adj, x)
return x
class GCNRecommender(nn.Module):
def __init__(self, num_users, num_items, hidden_size):
super(GCNRecommender, self).__init__()
self.user_embedding = nn.Embedding(num_users, hidden_size)
self.item_embedding = nn.Embedding(num_items, hidden_size)
self.conv1 = GraphConv(hidden_size, hidden_size)
self.conv2 = GraphConv(hidden_size, hidden_size)
self.fc = nn.Linear(hidden_size, 1)
def forward(self, user_ids, item_ids, adj):
user_emb = self.user_embedding(user_ids)
item_emb = self.item_embedding(item_ids)
x = torch.cat([user_emb, item_emb], dim=0)
x = F.relu(self.conv1(x, adj))
x = F.relu(self.conv2(x, adj))
x = self.fc(x)
return x.squeeze()
# 数据准备
num_users = 100
num_items = 200
hidden_size = 64
adj_matrix = torch.randn(num_users+num_items, num_users+num_items)
user_ids = torch.randint(0, num_users, (1000,))
item_ids = torch.randint(num_users, num_users+num_items, (1000,))
ratings = torch.randn(1000)
# 模型训练
dataset = torch.utils.data.TensorDataset(user_ids, item_ids, ratings)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
model = GCNRecommender(num_users, num_items, hidden_size)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(10):
for user_ids_batch, item_ids_batch, ratings_batch in dataloader:
optimizer.zero_grad()
preds = model(user_ids_batch, item_ids_batch, adj_matrix)
loss = F.mse_loss(preds, ratings_batch)
loss.backward()
optimizer.step()
print(f"epoch {epoch}: loss={loss.item()}")
# 推荐
user_ids_test = torch.randint(0, num_users, (100,))
item_ids_test = torch.randint(num_users, num_users+num_items, (100,))
preds_test = model(user_ids_test, item_ids_test, adj_matrix)
```
这个模型使用 GraphConv 层来处理用户和商品的特征,使用均方误差(MSE)作为损失函数,使用 Adam 作为优化器。在训练完成后,可以使用训练好的模型来进行推荐。