PHP and coreseek are combined to create an efficient e-commerce platform product recommendation engine

PHP and coreseek are combined to create an efficient e-commerce platform product recommendation engine

With the continuous development of Internet technology, e-commerce platforms have increasingly become one of the important channels for people to shop. In order to meet user needs, e-commerce platforms need an efficient product recommendation engine to display personalized product recommendations to users. This article will introduce how to use PHP and coreseek to quickly build an efficient e-commerce platform product recommendation engine.

1. Understand coreseek

coreseek is a full-text search server developed based on the open source search engine Sphinx. It has the capabilities of efficient distributed search and real-time index update, and also provides rich API and configuration options to facilitate developers for secondary development.

2. Installation and configuration coreseek

First, we need to install coreseek on the server. For specific installation methods, please refer to the official documentation of coreseek. After the installation is complete, we need to perform core configuration. In the configuration file sphinx.conf, you can set the index source, search source, index field, etc.

3. Data preparation

Before building a product recommendation engine, we need to prepare some product data. You can use a database as a data source to store product information in a table. This product information includes product name, price, description, category, etc.

4. Building a product index

We can index product data through the API provided by coreseek. In PHP code, you can use the SphinxClient class for index creation and data query. The following is an example showing how to build a product index:

<?php
require('sphinxapi.php');

$client = new SphinxClient();
$client->setServer("localhost", 9312);

// 添加商品索引
$client->index('products'); // 索引名
$client->setFieldWeights([100]); // 字段权重
$client->setMatchMode(SPH_MATCH_EXTENDED); // 匹配模式
$client->setRankingMode(SPH_RANK_PROXIMITY_BM25); // 排名模式

// 获取商品数据
$products = get_product_data();// 获取商品数据的函数

foreach ($products as $product) {
    $client->addDocument([
        'product_id' => $product['id'],
        'product_name' => $product['name'],
        'product_price' => $product['price'],
        'product_description' => $product['description'],
        'product_category' => $product['category']
    ]);
}

// 进行索引的更新
$client->updateAttributes('products');

// 索引完成后，可以进行查询操作
$res = $client->query('手机'); // 查询关键词为手机的商品

if ($res['total'] > 0) {
    foreach ($res['matches'] as $match) {
        $product_id = $match['id'];
        $product_name = $products[$product_id]['name'];
        // 展示商品信息
        echo '商品名称：' . $product_name;
    }
}
?>

The above code implements the function of establishing a product index and keyword search through the SphinxClient class. When building the index, we need to save the product data according to the structure of the index field. When querying, you can search for matching products based on keywords and display corresponding information.

5. Product recommendation

In practical applications, e-commerce platforms not only need to provide product search functions, but also need to implement personalized product recommendations. In order to implement product recommendations, we can make recommendations based on the user's historical purchase records, browsing behavior and other information.

The following is a sample code to implement product recommendation of similar categories:

<?php
// 获取当前用户ID
$user_id = 1;

// 获取当前用户购买过的商品类别
$purchased_categories = get_user_purchased_categories($user_id); // 获取用户购买过的商品类别的函数

$res = $client->query('category:' . implode(' | category:', $purchased_categories), 'products');

if ($res['total'] > 0) {
    foreach ($res['matches'] as $match) {
        $product_id = $match['id'];
        $product_name = $products[$product_id]['name'];
        // 展示商品信息
        echo '商品名称：' . $product_name;
    }
}
?>

The above code queries the product categories that the user has purchased and recommends products based on the category. First, get the product categories that the user has purchased. Then, query by category and display recommended product information.

Through the combination of PHP and coreseek, we can quickly build an efficient e-commerce platform product recommendation engine. The above example code only involves basic index construction and product recommendation. In actual applications, more functions can be expanded and optimized according to needs. I hope this article will help you build a product recommendation engine.

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