How to use PHP to build user shopping behavior analysis and recommendation model

How to use PHP to build a user shopping behavior analysis and recommendation model

In the Internet era, user shopping behavior has become an important research object for major e-commerce platforms. By analyzing users' purchase records, we can understand users' preferences and needs, and make product recommendations based on user behavior to improve user satisfaction and purchase rates. This article will introduce how to use PHP to build a simple user shopping behavior analysis and recommendation model, with code examples.

1. Data collection and preprocessing
   First, we need to collect user purchase records from the database of the e-commerce platform. You can use SQL statements to query the database and store the results in an array or object. In the code example, we assume that the purchase records are stored in an array called purchases.

$purchases = array(
    array('user_id' => 1, 'product_id' => 'A'),
    array('user_id' => 1, 'product_id' => 'B'),
    array('user_id' => 2, 'product_id' => 'C'),
    array('user_id' => 3, 'product_id' => 'A'),
    // ... 其他购买记录
);

Then, we can perform some data preprocessing operations, such as filtering out users and products with fewer purchases, or numbering users and products, etc. In the code example, we use a two-dimensional array to store the user and item numbers respectively.

$users = array();
$products = array();

foreach ($purchases as $purchase) {
    $user_id = $purchase['user_id'];
    $product_id = $purchase['product_id'];

    if (!isset($users[$user_id])) {
        $users[$user_id] = count($users) + 1;
    }

    if (!isset($products[$product_id])) {
        $products[$product_id] = count($products) + 1;
    }
}

2. Building a shopping behavior analysis model
   Next, we can build a shopping behavior analysis model, such as an association rule analysis model. Association rule analysis can mine the correlation between users when purchasing products and recommend other related products based on the products purchased by users.

In the code example, we use a two-dimensional array transactions to store each user's purchase record. Then, the support and confidence between items are calculated by traversing the purchase records, and stored in an associative array rules.

$transactions = array();

foreach ($purchases as $purchase) {
    $user_id = $purchase['user_id'];
    $product_id = $purchase['product_id'];

    if (!isset($transactions[$user_id])) {
        $transactions[$user_id] = array();
    }

    $transactions[$user_id][] = $product_id;
}

$rules = array();

foreach ($transactions as $transaction) {
    $count = count($transaction);

    for ($i = 0; $i < $count - 1; $i++) {
        $item_i = $transaction[$i];

        for ($j = $i+1; $j < $count; $j++) {
            $item_j = $transaction[$j];
            
            if (!isset($rules[$item_i][$item_j])) {
                $rules[$item_i][$item_j] = 1;
            } else {
                $rules[$item_i][$item_j]++;
            }
        }
    }
}

// 计算支持度和置信度

foreach ($rules as $item_i => $rule) {
    foreach ($rule as $item_j => $count) {
        $support = $count / $users_count;
        $confidence = $count / $products_count[$item_i];

        // 存储支持度和置信度

        $rules[$item_i][$item_j] = array(
            'support' => $support,
            'confidence' => $confidence
        );
    }
}

3. Recommendation based on shopping behavior model
   Finally, we can recommend products based on shopping behavior model. For example, for a product that a user has purchased, other related products can be recommended based on the association rule model.

In the code example, we give a function recommendProducts, which queries the association rule model and returns recommended results based on the products the user has purchased.

function recommendProducts($user_id) {
    global $rules;
    global $transactions;
    global $products;

    $transaction = $transactions[$user_id];
    $recommendations = array();

    foreach ($transaction as $item_i) {
        if (isset($rules[$item_i])) {
            foreach ($rules[$item_i] as $item_j => $rule) {
                if (!in_array($item_j, $transaction)) {
                    $recommendations[$item_j] = $rule['confidence'];
                }
            }
        }
    }

    // 按推荐度排序

    arsort($recommendations);

    // 返回推荐结果

    return array_keys($recommendations);
}

// 示例使用

$user_id = 1;
$recommendations = recommendProducts($user_id);

echo "为用户 $user_id 推荐的商品：";
foreach ($recommendations as $product_id) {
    echo $products[$product_id] . " ";
}

Through the above steps, we have completed the construction of a simple user shopping behavior analysis and recommendation model. Of course, this is just a simple example, and actual shopping behavior analysis and recommendation models may be more complex and large. However, this example can serve as a primer and give you ideas for building more complex models.

Summary:
This article introduces the method of using PHP to build a user shopping behavior analysis and recommendation model, and comes with relevant code examples. This model can analyze the user's preferences and needs based on the user's purchase record, and then recommend products based on association rules. I hope this article can provide you with some reference on shopping behavior analysis and recommendation model construction, and help you better understand and apply knowledge in this field.

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