1. 引言
预测广告点击率（CTR）是在线广告系统中的一个关键任务。标题和图片是影响CTR的重要因素。以下教程将展示如何使用高级方法结合标题和图片特征来预测CTR。
2. 环境准备
首先，确保你已经安装了必要的Python库：
pip install pandas numpy scikit-learn xgboost transformers torch torchvision
3. 数据准备
假设你有一个包含广告标题、图片和点击数据的数据集，名为ad_data.csv。数据集的结构如下：
- 标题 (
title) - 图片路径 (
image_path) - 点击标签 (
clicked)
4. Python代码示例
以下代码示例展示了如何使用BERT和Swin Transformer结合标题和图片特征来预测CTR：
import pandas as pd
import torch
from transformers import BertTokenizer, BertModel
from torchvision import models, transforms
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.optim as optim
# 加载数据集
data = pd.read_csv('ad_data.csv')
# 定义数据集类
class AdDataset(Dataset):
def __init__(self, data, tokenizer, transform):
self.data = data
self.tokenizer = tokenizer
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
title = self.data.iloc[idx, 0]
image_path = self.data.iloc[idx, 1]
clicked = self.data.iloc[idx, 2]
# 处理标题
inputs = self.tokenizer(title, return_tensors='pt', max_length=512, padding='max_length', truncation=True)
title_ids = inputs['input_ids'].squeeze()
attention_mask = inputs['attention_mask'].squeeze()
# 处理图片
image = Image.open(image_path)
image = self.transform(image)
image = image.unsqueeze(0)
return {
'title_ids': title_ids,
'attention_mask': attention_mask,
'image': image,
'clicked': torch.tensor(clicked, dtype=torch.long)
}
# 初始化BERT和Swin模型
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
bert_model = BertModel.from_pretrained('bert-base-uncased')
swin_model = models.swin_bert_tiny(weights='IMAGENET1K_V1')
# 定义自定义模型
class AdModel(nn.Module):
def __init__(self):
super(AdModel, self).__init__()
self.bert = bert_model
self.swin = swin_model
self.dropout = nn.Dropout(0.1)
self.fc = nn.Linear(768 + 768, 2)
def forward(self, title_ids, attention_mask, image):
title_features = self.bert(title_ids, attention_mask=attention_mask).pooler_output
image_features = self.swin(image).flatten()
combined_features = torch.cat((title_features, image_features), dim=1)
output = self.dropout(combined_features)
output = self.fc(output)
return output
# 初始化数据集和数据加载器
dataset = AdDataset(data, tokenizer, transform)
batch_size = 32
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = AdModel().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-5)
for epoch in range(5):
model.train()
total_loss = 0
for batch in data_loader:
title_ids = batch['title_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
image = batch['image'].to(device)
clicked = batch['clicked'].to(device)
optimizer.zero_grad()
outputs = model(title_ids, attention_mask, image)
loss = criterion(outputs, clicked)
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f'Epoch {epoch+1}, Loss: {total_loss / len(data_loader)}')
# 评估模型
model.eval()
with torch.no_grad():
total_correct = 0
for batch in data_loader:
title_ids = batch['title_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
image = batch['image'].to(device)
clicked = batch['clicked'].to(device)
outputs = model(title_ids, attention_mask, image)
_, predicted = torch.max(outputs, dim=1)
total_correct += (predicted == clicked).sum().item()
accuracy = total_correct / len(data)
print(f'Test Accuracy: {accuracy:.4f}')
5. 总结
通过结合BERT和Swin Transformer处理标题和图片特征，可以显著提高CTR预测的准确性。这种方法利用多模态特征融合技术，能够更好地捕捉用户的偏好和行为。
案例示例
- CTR驱动的广告图像生成：利用多模态大语言模型生成CTR优化的广告图像，可以提高广告的点击率
- 深度CTR预测模型：使用深度神经网络直接从原始图像像素和其他特征预测CTR，提高了预测的准确性
