深度学习+用户行为预测：揭秘数据背后的故事

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

# 模拟用户行为数据
data = {
    '用户ID': [1, 1, 1, 2, 2],
    '行为序列': ['点击', '添加到购物车', '购买', '点击', '点击'],
    '标签': [1, 1, 1, 0, 0]  # 1代表购买，0代表未购买
}

df = pd.DataFrame(data)

# 将行为序列映射为数值
behavior_mapping = {'点击': 0, '添加到购物车': 1, '购买': 2}
df['行为序列'] = df['行为序列'].map(behavior_mapping)

# 构造特征和标签
X = df['行为序列'].values.reshape(-1, 1)  # 示例化特征（实际数据需更复杂）
y = df['标签'].values

# 数据拆分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 构建模型
model = Sequential([
    LSTM(64, input_shape=(None, 1), activation='relu'),
    Dense(1, activation='sigmoid')  # 二分类问题
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 模拟训练数据
X_train = np.random.rand(100, 10, 1)  # 假设100条行为序列，长度为10
y_train = np.random.randint(2, size=100)

# 训练模型
model.fit(X_train, y_train, epochs=5, batch_size=16)

# 模拟测试数据
X_test = np.random.rand(20, 10, 1)
predictions = model.predict(X_test)