Product recommendation and personalized search based on Elasticsearch in PHP

Title: Product recommendation and personalized search based on Elasticsearch in PHP

Article text:
Introduction:
With the continuous development of e-commerce, Product recommendations and personalized searches are becoming increasingly important. Users hope to quickly find products that meet their needs among a large number of products, and merchants also need to display the most suitable products to users through recommendation systems. In PHP development, using Elasticsearch to implement product recommendation and personalized search is one of the more common solutions. This article will introduce how to implement product recommendation and personalized search based on Elasticsearch, and provide specific code examples.

1. Basic concepts of Elasticsearch
Elasticsearch is an open source, high-performance search engine that can quickly index and search massive amounts of data. Before using Elasticsearch, we need to understand the following basic concepts:

1.1 Index (Index): Similar to the database in a relational database, the index is a logical concept used to group and organize data. An index can contain multiple types (Type).

1.2 Type: Similar to tables in relational databases, each type has its own fields and attributes.

1.3 Document: Similar to rows or records in relational databases, documents are the basic unit of Elasticsearch storage.

1.4 Mapping: Similar to the schema in a relational database, mapping is used to define the types and attributes of fields.

2. Implementation of product recommendation
In product recommendation, we can use Elasticsearch's relevance score to achieve it. The specific steps are as follows:

2.1 Create index and type
First, we need to create an index to store product data and define a type to describe product information. You can use the PHP client provided by Elasticsearch to connect to the Elasticsearch server and execute the following code:

require 'vendor/autoload.php';

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

$params = [
    'index' => 'product',
    'body' => [
        'mappings' => [
            'type' => [
                'properties' => [
                    'name' => [
                        'type' => 'text',
                        'analyzer' => 'standard'
                    ],
                    'category' => [
                        'type' => 'keyword'
                    ],
                    'price' => [
                        'type' => 'double'
                    ]
                ]
            ]
        ]
    ]
];

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

2.2 Import product data
Next, we need to import the product data into Elasticsearch. You can use the batch insertion function of Elasticsearch to import product data into Elasticsearch in batches. The sample code is as follows:

$products = [
    ['name' => 'iPhone 12', 'category' => '手机', 'price' => 7999],
    ['name' => 'MacBook Pro', 'category' => '电脑', 'price' => 10999],
    ['name' => 'AirPods Pro', 'category' => '耳机', 'price' => 1999]
];

$params = [
    'index' => 'product',
    'type' => 'type',
    'body' => []
];

foreach ($products as $product) {
    $params['body'][] = [
        'index' => [
            '_index' => 'product',
            '_type' => 'type'
        ]
    ];
    
    $params['body'][] = $product;
}

$client->bulk($params);

2.3 Product recommendation query
In order to implement product recommendation, we can build a query based on the user's current sentiment. If you are interested in a product, search for other products that are more closely related to it. The sample code is as follows:

$params = [
    'index' => 'product',
    'type' => 'type',
    'body' => [
        'query' => [
            'more_like_this' => [
                'fields' => ['name'],
                'like' => [
                    ['_index' => 'product', '_id' => '1'] // 假设用户感兴趣的商品编号为 1
                ],
                'min_term_freq' => 1,
                'max_query_terms' => 20
            ]
        ]
    ]
];

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

The above code will return a set of other products that are highly related to the products that the user is interested in.

3. Personalized search implementation
Personalized search can provide users with more personalized search results based on their search habits and preferences. The steps to implement personalized search are as follows:

3.1 Record user search history
First, we need to record the user's search history. You can use Redis or a database to store the user's search history. The sample code is as follows:

$userId = 1; // 假设用户编号为 1
$keyword = 'iPhone';

// 存储用户搜索记录
$redis->sadd("user:$userId:search_history", $keyword);

3.2 Constructing a personalized search query
Based on the user's search history, we can construct a query that uses keywords frequently searched by the user as query conditions. The sample code is as follows:

$params = [
    'index' => 'product',
    'type' => 'type',
    'body' => [
        'query' => [
            'bool' => [
                'should' => [
                    ['match' => ['name' => $keyword1]], // 用户搜索历史中的关键词1
                    ['match' => ['name' => $keyword2]], // 用户搜索历史中的关键词2
                    // ...
                ]
            ]
        ]
    ]
];

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

The above code will return personalized search results built based on the user's search history.

Conclusion:
By using Elasticsearch, we can easily implement product recommendation and personalized search functions. This article introduces the method of implementing product recommendation and personalized search based on Elasticsearch, and provides specific PHP code examples. I hope this article can provide some help to PHP developers in product recommendation and personalized search.

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