使用Python实现深度学习模型：个性化推荐与广告优化

deep_learning_recommender/
│
├── data/
│   ├── interactions.csv
│   ├── items.csv
│   └── users.csv
│
├── model/
│   ├── __init__.py
│   ├── data_preprocessing.py
│   ├── model.py
│   └── train.py
│
├── app/
│   ├── __init__.py
│   ├── recommender.py
│   └── routes.py
│
├── templates/
│   └── index.html
│
├── app.py
└── requirements.txt

user_id,item_id,interaction
1,101,1
1,102,0
2,101,1
2,103,1
...

item_id,item_name,category
101,Item A,Category 1
102,Item B,Category 2
103,Item C,Category 1
...

user_id,user_name,age,gender
1,Alice,25,F
2,Bob,30,M
...

import pandas as pd

# 加载数据
interactions = pd.read_csv('data/interactions.csv')
items = pd.read_csv('data/items.csv')
users = pd.read_csv('data/users.csv')

# 查看数据
print(interactions.head())
print(items.head())
print(users.head())

import pandas as pd
from sklearn.model_selection import train_test_split

def load_data():
    interactions = pd.read_csv('data/interactions.csv')
    items = pd.read_csv('data/items.csv')
    users = pd.read_csv('data/users.csv')
    return interactions, items, users

def preprocess_data(interactions, items, users):
    # 合并数据
    data = interactions.merge(users, on='user_id').merge(items, on='item_id')
    # 编码
    data['user_id'] = data['user_id'].astype('category').cat.codes
    data['item_id'] = data['item_id'].astype('category').cat.codes
    # 分割数据集
    X = data[['user_id', 'item_id']]
    y = data['interaction']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, Flatten, Dot, Dense

def create_model(num_users, num_items):
    user_input = Input(shape=(1,))
    item_input = Input(shape=(1,))
    
    user_embedding = Embedding(input_dim=num_users, output_dim=50)(user_input)
    item_embedding = Embedding(input_dim=num_items, output_dim=50)(item_input)
    
    user_vec = Flatten()(user_embedding)
    item_vec = Flatten()(item_embedding)
    
    dot_product = Dot(axes=1)([user_vec, item_vec])
    
    output = Dense(1, activation='sigmoid')(dot_product)
    
    model = Model(inputs=[user_input, item_input], outputs=output)
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
    return model

from model.data_preprocessing import load_data, preprocess_data
from model.model import create_model

# 加载和预处理数据
interactions, items, users = load_data()
X_train, X_test, y_train, y_test = preprocess_data(interactions, items, users)

# 创建模型
num_users = interactions['user_id'].nunique()
num_items = interactions['item_id'].nunique()
model = create_model(num_users, num_items)

# 训练模型
model.fit([X_train['user_id'], X_train['item_id']], y_train, epochs=10, batch_size=64, validation_data=([X_test['user_id'], X_test['item_id']], y_test))

# 保存模型
model.save('model/recommender_model.h5')

from flask import Flask

app = Flask(__name__)

from app import routes

import pandas as pd
import tensorflow as tf

def load_model():
    model = tf.keras.models.load_model('model/recommender_model.h5')
    return model

def recommend_items(user_id, model, interactions, items):
    user_interactions = interactions[interactions['user_id'] == user_id]
    user_items = user_interactions['item_id'].unique()
    all_items = items['item_id'].unique()
    candidate_items = [item for item in all_items if item not in user_items]
    
    user_ids = [user_id] * len(candidate_items)
    predictions = model.predict([user_ids, candidate_items])
    
    recommended_items = items[items['item_id'].isin(candidate_items)]
    recommended_items['score'] = predictions
    
    return recommended_items.sort_values(by='score', ascending=False)

from flask import render_template, request
from app import app
from app.recommender import load_model, recommend_items
import pandas as pd

model = load_model()
interactions = pd.read_csv('data/interactions.csv')
items = pd.read_csv('data/items.csv')

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/recommend', methods=['POST'])
def recommend():
    user_id = int(request.form['user_id'])
    recommendations = recommend_items(user_id, model, interactions, items)
    return render_template('index.html', recommendations=recommendations)

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>个性化推荐系统</title>
</head>
<body>
    <h1>个性化推荐系统</h1>
    <form action="/recommend" method="post">
        <label for="user_id">用户ID:</label>
        <input type="text" id="user_id" name="user_id">
        <button type="submit">推荐</button>
    </form>
    {% if recommendations %}
        <h2>推荐结果:</h2>
        <ul>
            {% for item in recommendations.itertuples() %}
                <li>{{ item.item_name }} - {{ item.category }} - {{ item.score }}</li>
            {% endfor %}
        </ul>
    {% endif %}
</body>
</html>

from app import app

if __name__ == '__main__':
    app.run(debug=True)