How to conduct user purchasing behavior analysis and personalized recommendations for PHP flash sale system

How to conduct user purchasing behavior analysis and personalized recommendations of the PHP flash sale system requires specific code examples

With the in-depth development of the Internet, competition in the e-commerce field has changed. It's getting more and more intense. In order to attract users and encourage them to purchase, e-commerce platforms often launch flash sales activities. However, it is not easy for users to choose and purchase products that suit them. Therefore, it is very important to analyze users' purchasing behavior and recommend personalized products for them.

In the PHP flash sale system, we can conduct user purchasing behavior analysis and personalized recommendations through the following steps:

1. Data collection: First, we need to collect data on user behavior collection. It can record the user's browsing, collection, adding shopping cart and purchasing behaviors in the system, as well as the user's personal information and preferences.
2. Data storage: Store the collected data in the database. Relational databases such as MySQL can be used to store user data.
3. Data analysis: Through data analysis, we can understand users’ purchasing habits, preferences and potential needs. By analyzing the user's purchase frequency, purchase time, purchase amount and other indicators, the user's purchasing behavior pattern can be derived. In addition, the user's preference characteristics can also be obtained by analyzing the user's historical purchase records and personal information, such as the user's preference for brand, color, size, etc.
4. Recommendation algorithm: Based on the user's purchasing behavior and personal preferences, we can use the recommendation algorithm to recommend personalized products to the user. Commonly used recommendation algorithms include collaborative filtering recommendation algorithms, content-based recommendation algorithms and hybrid recommendation algorithms. The following takes the collaborative filtering recommendation algorithm as an example to introduce the specific implementation method.

The implementation steps of the collaborative filtering recommendation algorithm are as follows:

Step 1: Calculate the similarity between users. A user-based collaborative filtering algorithm can be used to measure the correlation between users by calculating the similarity matrix between users.

Step 2: Recommend products to users. When a user browses, collects, adds to a shopping cart, or purchases products, products that similar users like can be recommended to the user based on the user's purchasing behavior and the purchasing behavior of similar users.

The following is a simple sample code that demonstrates how to implement a user-based collaborative filtering recommendation algorithm:

// 获取用户ID
$userID = $_SESSION['userID'];

// 从数据库中获取用户与商品的交互行为数据
$interactions = fetch_interactions($userID);

// 计算用户之间的相似度矩阵
$similarityMatrix = calculate_similarity_matrix($interactions);

// 为用户推荐商品
$recommendedItems = recommend_items($similarityMatrix, $userID);

// 展示推荐的商品
foreach ($recommendedItems as $itemID) {
    $item = fetch_item($itemID);
    echo "商品名称：{$item['name']}, 价格：{$item['price']}";
}

// 从数据库中获取用户与商品的交互行为数据
function fetch_interactions($userID) {
    // 查询数据库获取用户与商品的交互行为数据
    // 返回用户与商品的交互行为数据数组，数组的每个元素包含用户ID、商品ID和操作类型（浏览、收藏、加购物车、购买等）
}

// 计算用户之间的相似度矩阵
function calculate_similarity_matrix($interactions) {
    // 根据用户与商品的交互行为数据计算用户之间的相似度矩阵
    // 返回用户之间的相似度矩阵
}

// 为用户推荐商品
function recommend_items($similarityMatrix, $userID) {
    // 根据用户之间的相似度矩阵和用户ID推荐商品
    // 返回推荐的商品ID数组
}

// 从数据库中获取商品信息
function fetch_item($itemID) {
    // 查询数据库获取商品信息
    // 返回商品信息数组，数组的每个元素包含商品ID、商品名称和商品价格等信息
}

Through the above code example, we can implement a user-based collaborative filtering recommendation algorithm and based on The user's purchasing behavior and the purchasing behavior of similar users recommend personalized products to the user.

To sum up, by analyzing the user’s purchasing behavior and using the recommendation algorithm to recommend personalized products to the user, the user’s purchasing experience can be improved and the user’s purchasing rate can be increased. For the PHP flash sale system, purchasing behavior analysis and personalized recommendations are very important functions, which can help the platform attract more users and improve user purchase satisfaction.

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