Second-hand recycling website uses similar product recommendation function developed in PHP

Similar product recommendation function developed by second-hand recycling website using PHP

With the development of society, people are becoming more and more aware of resource conservation and environmental protection, and the second-hand recycling market is gradually emerging. As an important platform, second-hand recycling websites can help people reuse items and reduce waste, while also creating economic benefits for people. In order to improve user experience and allow users to better find products they are interested in, we can develop a similar product recommendation function.

This article will introduce to you how to use PHP to develop a similar product recommendation function. First, we need to clarify how to determine whether two items are similar. A simple way is to compare items by category, brand, quality and other attributes. In actual development, we can use machine learning methods to determine the similarity of items by analyzing and calculating user behavior data. However, this method is more complicated. This article mainly introduces an attribute-based method.

First, we need to add a similarity field to the product table. The code is as follows:

ALTER TABLE `product` ADD `similarity` INT(11) NOT NULL DEFAULT '0';

Next, we need to record the user's behavior data when the user browses the product. To simplify the example, assume that our user ID is 1 and the product ID being viewed is 100. The code is as follows:

INSERT INTO `user_browsing_history` (`user_id`, `product_id`) VALUES ('1', '100');

Then, we can write a function to calculate the similarity of items. The code is as follows:

function calculateSimilarity($product_id) {
    // 获取当前商品的所有属性
    $current_product = getProduct($product_id);
    
    // 获取所有用户浏览过的商品
    $user_browsing_history = getUserBrowsingHistory(1);
    
    // 计算各个商品的相似度
    foreach ($user_browsing_history as $history) {
        $similar_product = getProduct($history['product_id']);
        
        // 计算相似度
        $similarity = 0;
        if ($current_product['category'] == $similar_product['category']) {
            $similarity += 30;
        }
        if ($current_product['brand'] == $similar_product['brand']) {
            $similarity += 20;
        }
        if ($current_product['condition'] == $similar_product['condition']) {
            $similarity += 50;
        }
        
        // 更新相似度字段
        updateProductSimilarity($history['product_id'], $similarity);
    }
}

Finally, on the product details page, we can make recommendations based on the similarity field. The code is as follows:

function getSimilarProducts($product_id) {
    $similar_products = array();
    $current_product = getProduct($product_id);
    
    // 获取相似度最高的商品
    $query = "SELECT * FROM `product` WHERE `id` != '$product_id' ORDER BY `similarity` DESC LIMIT 5";
    $result = mysqli_query($conn, $query);
    
    while ($row = mysqli_fetch_assoc($result)) {
        $similar_products[] = $row;
    }
    
    return $similar_products;
}

Through the above code example, we can implement an attribute-based similar product recommendation function. When a user browses a product, we can calculate the similarity between the product and other products based on the user's behavior records, and save the similarity results in the similarity field in the product table. On the product details page, we can use the similarity field to obtain several products that are most similar to the current product for recommendation.

To sum up, the process of using PHP to develop a similar product recommendation function involves many aspects such as database operations, similarity calculation, and recommendation algorithms. Through this function, we can help users better find the products they are interested in and improve the user experience of the website. Of course, this is just a simple example. In actual projects, more complex development needs to be carried out according to specific needs to make the recommendation function more intelligent and accurate.

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