How to build an intelligent advertising recommendation system using Elasticsearch and PHP

How to use Elasticsearch and PHP to build an intelligent advertising recommendation system

The intelligent advertising recommendation system plays an important role in today's Internet advertising industry. It provides users with personalized advertising recommendations by analyzing users' behavior, interests and preferences to improve advertising effectiveness and user experience. In this article, we will introduce how to build an intelligent advertising recommendation system using Elasticsearch and PHP.

Elasticsearch is an open source distributed search and analysis engine. It is fast, scalable, and highly available, and is very suitable for building intelligent advertising recommendation systems. And PHP is a popular server-side scripting language used for developing web applications. Below we will use Elasticsearch and PHP to build our intelligent advertising recommendation system.

First, we need to prepare some data. Suppose we have an advertising system, which contains three key entities: advertisers, advertising slots, and users. We can use Elasticsearch for data storage and indexing to support fast query and analysis.

Next, we need to define some key indexes and mappings. In Elasticsearch, an index can be viewed as a database, and mapping defines the data type and structure in the index. We can create and update indexes and mappings through Elasticsearch's PHP client library.

require 'vendor/autoload.php';

use ElasticsearchClientBuilder;

$client = ClientBuilder::create()->build();

$params = [
    'index' => 'advertisements',
    'body' => [
        'mappings' => [
            'properties' => [
                'title' => [
                    'type' => 'text'
                ],
                'content' => [
                    'type' => 'text'
                ],
                'tag' => [
                    'type' => 'keyword'
                ],
                'user_id' => [
                    'type' => 'integer'
                ]
            ]
        ]
    ]
];

$response = $client->indices()->create($params);

The above code example creates an index named "advertisements" and defines the mapping of the four fields "title", "content", "tag" and "user_id".

Next, we can use Elasticsearch to implement the advertising recommendation algorithm. Commonly used advertising recommendation algorithms include content-based recommendations, collaborative filtering recommendations, and user behavior-based recommendations. Here we take content-based recommendations as an example. We can use Elasticsearch's full-text search function to match the user's interests and the content of the advertisement to find the most relevant advertisements.

$params = [
    'index' => 'advertisements',
    'body' => [
        'query' => [
            'match' => [
                'tag' => 'sports'
            ]
        ]
    ]
];

$response = $client->search($params);

The above code example uses the full-text search function to find advertisements whose "tag" field matches "sports" in the "advertisements" index. We can dynamically adjust query conditions based on the user's interest tags to achieve personalized advertising recommendations.

Finally, we need to display the recommended results to the user. In PHP, we can use web frameworks for development, write corresponding controller and view codes, and present recommended results to users.

// 控制器代码

public function recommend()
{
    $user_id = $_SESSION['user_id'];

    // 查询用户的兴趣标签
    $interests = $this->userModel->getInterests($user_id);

    // 使用Elasticsearch进行广告推荐
    $params = [
        'index' => 'advertisements',
        'body' => [
            'query' => [
                'terms' => [
                    'tag' => $interests
                ]
            ]
        ]
    ];

    $response = $this->client->search($params);

    $advertisements = $response['hits']['hits'];

    // 渲染视图，将推荐结果呈现给用户
    $this->view('recommend', ['advertisements' => $advertisements]);
}

// 视图代码

foreach ($advertisements as $advertisement) {
    echo "<div class='advertisement'>";
    echo "<h2>{$advertisement['_source']['title']}</h2>";
    echo "<p>{$advertisement['_source']['content']}</p>";
    echo "</div>";
}

The above code example demonstrates how to present the recommended results to the user. We first query the user's interest tags, and then use Elasticsearch for ad recommendation. Finally, use HTML and CSS to display the recommended results to users.

The actual intelligent advertising recommendation system also needs to handle more details and complex scenarios. But this article provides a basic framework and sample code that allows you to use Elasticsearch and PHP to build a simple intelligent advertising recommendation system. Hope this helps!

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