How to use PHP to implement intelligent recommendations and personalized recommendations

How to use PHP to implement intelligent recommendation and personalized recommendation functions

Introduction:
In today’s Internet era, personalized recommendation systems have been widely used in various fields. Such as e-commerce, social media and news information, etc. Intelligent recommendation and personalized recommendation functions play an important role in improving user experience, increasing user stickiness and increasing conversion rate. This article will introduce how to use PHP to implement intelligent recommendation and personalized recommendation functions, and provide relevant code examples.

1. Principle of Intelligent Recommendation
Intelligent recommendation automatically recommends relevant content based on the user’s historical behavior and personal interests. It is mainly based on the following principles:

1. Collaboration Filtering (Collaborative Filtering): By analyzing the user's historical behavior and interests, find other users who are similar to them, and recommend content based on these users' past preferences;
2. Content Filtering (Content Filtering): By analyzing items Based on the similarity between items, recommend items similar to the user's past interests;
3. Hybrid Recommendation: Comprehensive use of collaborative filtering and content filtering methods to make recommendations.

2. Intelligent recommendation implementation
In PHP, to implement intelligent recommendation and personalized recommendation functions, you can use a database to store user behavior data and item information, and use algorithms to perform recommendation calculations. The following are the general steps to implement the intelligent recommendation function:

1. Create database tables
   First, create two database tables, one to save user behavior data, such as user ID, item ID, behavior type etc.; the other is used to save item information, such as item ID, name, description, etc.

CREATE TABLE `user_action` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `user_id` int(10) unsigned NOT NULL,
  `item_id` int(10) unsigned NOT NULL,
  `action_type` tinyint(4) NOT NULL,
  `action_time` datetime NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

CREATE TABLE `item_info` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `item_id` int(10) unsigned NOT NULL,
  `name` varchar(255) NOT NULL,
  `description` text NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

2. Collect user behavior data
   On the website or application, collect user behavior data such as browsing, purchasing, and collecting, and store it in the database.

// 用户浏览商品
function userBrowseItem($user_id, $item_id) {
    // 添加用户浏览记录到数据库
    $sql = "INSERT INTO user_action (user_id, item_id, action_type, action_time) 
            VALUES ($user_id, $item_id, 1, NOW())";
    // 执行SQL语句
}

// 用户购买商品
function userBuyItem($user_id, $item_id) {
    // 添加用户购买记录到数据库
    $sql = "INSERT INTO user_action (user_id, item_id, action_type, action_time) 
            VALUES ($user_id, $item_id, 2, NOW())";
    // 执行SQL语句
}

3. Calculate item similarity
   Based on user behavior data and item information, to calculate the similarity between items, you can use a content-based recommendation algorithm or collaborative filtering algorithm.

// 计算物品相似度
function calculateItemSimilarity($item_id_1, $item_id_2) {
    // 根据商品特征计算相似度
    // 返回相似度值
}

4. Recommendation algorithm
   Using the calculated item similarity and the user's historical behavior data, collaborative filtering, content filtering or hybrid recommendation methods are used to perform recommendation calculations and return Recommended results.

// 根据用户行为数据进行推荐
function recommendItems($user_id) {
    // 获取用户的浏览、购买等行为数据
    $sql = "SELECT item_id, action_type FROM user_action WHERE user_id = $user_id";
    // 执行SQL语句，并根据用户的行为数据进行推荐计算
    // 返回推荐结果
}

3. Personalized recommendations
Personalized recommendations are based on the user’s personal interests and preferences, recommending content related to their preferences. To achieve personalized recommendations, users' personal interest data can be obtained through questionnaires when users register or through user feedback. The following are the general steps to implement the personalized recommendation function:

1. Collect user personalized data
   When the user registers or logs in, guide the user to fill in a questionnaire in the field of personal interest or provide feedback collection methods.
2. Storing user personalized data
   Store the user's personalized data in the database, which can be represented by fields such as user ID and field of interest.

CREATE TABLE `user_interest` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `user_id` int(10) unsigned NOT NULL,
  `interest` varchar(255) NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

3. Recommendation based on user's personalized data
   Based on the recommendation algorithm, the recommendation calculation is performed based on the user's personalized data, so that the recommended results are more in line with the user's personal interests. and preferences.

// 根据用户个性化数据进行推荐
function personalizedRecommendation($user_id) {
    // 获取用户的个性化数据
    $sql = "SELECT interest FROM user_interest WHERE user_id = $user_id";
    // 获取用户的个性化数据，并根据个性化数据进行推荐计算
    // 返回个性化推荐结果
}

Conclusion:
This article introduces how to use PHP to implement intelligent recommendation and personalized recommendation functions. By collecting users' historical behavioral data and personalized data, and performing recommendation calculations based on recommendation algorithms, user experience can be improved, user stickiness and conversion rates can be increased. Although this article only provides a simple implementation method, through in-depth understanding and application of intelligent recommendation algorithms and personalized recommendation algorithms, a more accurate and effective recommendation system can be achieved.

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