Home > Article > Backend Development > 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:
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.
The above is the detailed content of How to conduct user purchasing behavior analysis and personalized recommendations for PHP flash sale system. For more information, please follow other related articles on the PHP Chinese website!