Evaluation of mall product recommendation algorithm developed using PHP

Evaluation of mall product recommendation algorithm developed using PHP

With the development of e-commerce, more and more mall websites have begun to use recommendation algorithms to provide personalized product recommendation services. As a commonly used server-side programming language, PHP is also widely used in the development of shopping mall websites. This article will introduce how to use the PHP developer mall product recommendation algorithm and evaluate it.

1. Basic Principle of Product Recommendation Algorithm

The goal of the product recommendation algorithm is to provide users with product recommendations that may be of interest based on the user’s behavioral data. Commonly used recommendation algorithms include user-based collaborative filtering, content-based recommendation and hybrid recommendation. Among them, the user-based collaborative filtering algorithm is the most commonly used algorithm.

The user-based collaborative filtering algorithm analyzes user behavior data to find users with similar behaviors to the target user, and then recommends products to the target user based on the products purchased by these users. This process can be divided into two steps: calculating the similarity between users and recommending products to the target users.

2. Use PHP to develop product recommendation algorithms

In PHP, you can use a database to store user behavior data, and use corresponding algorithms to implement product recommendation functions. The following is a simple PHP code example that demonstrates how to implement a user-based collaborative filtering algorithm.

First, you need to create a database table to store user behavior data. You can create a table named "user_behavior", containing fields such as "user ID", "item ID" and "behavior type".

CREATE TABLE user_behavior (
    user_id INT,
    item_id INT,
    action_type VARCHAR(50)
);

Then, PHP code needs to be written to calculate the similarity between users. Here is a simple example using cosine similarity to calculate similarity between users.

function cosine_similarity($user1, $user2) {
    // 获取用户1和用户2的行为数据
    $user1_behavior = get_user_behavior($user1);
    $user2_behavior = get_user_behavior($user2);
    
    // 计算用户1和用户2的行为向量
    $vector1 = calculate_vector($user1_behavior);
    $vector2 = calculate_vector($user2_behavior);
    
    // 计算余弦相似度
    $similarity = dot_product($vector1, $vector2) / (norm($vector1) * norm($vector2));
    
    return $similarity;
}

Finally, product recommendations need to be made for the target users based on their similarity. The following is a simple example that recommends products to target users based on similarity from high to low.

function recommend_items($target_user) {
    // 获取与目标用户相似度最高的用户
    $most_similar_user = get_most_similar_user($target_user);
    
    // 获取与目标用户相似度最高的用户购买过的商品
    $most_similar_user_items = get_user_items($most_similar_user);
    
    // 过滤掉目标用户已经购买过的商品
    $recommended_items = filter_items($most_similar_user_items, $target_user);
    
    return $recommended_items;
}

3. Evaluation of product recommendation algorithm

In actual use, the product recommendation algorithm needs to be evaluated to ensure its accuracy and effectiveness. Common methods for evaluating product recommendation algorithms include offline evaluation and online evaluation.

Offline evaluation is an evaluation conducted on historical data. The performance of the algorithm is evaluated by calculating indicators such as accuracy, recall, and coverage between recommended results and actual user behavior.

Online evaluation is an evaluation conducted in a real-time environment to evaluate the effectiveness of the algorithm by comparing new recommendation results with actual user feedback.

In summary, this article introduces how to use the PHP Developer City product recommendation algorithm and evaluate it. By implementing a user-based collaborative filtering algorithm and applying it to the mall website, personalized product recommendation services can be provided, thereby improving the user's shopping experience.

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