RiSearch PHP implements user personalized search ranking and recommendation

RiSearch PHP implements user personalized search ranking and recommendation, which requires specific code examples

With the development of the Internet and the explosive growth of data, users’ personalized needs are becoming more and more important. is becoming more and more important. Users hope to obtain content in search results that is more in line with their interests and preferences, but traditional search engines often can only provide keyword-based search results and cannot meet users' personalized needs. In order to solve this problem, we can use RISEARCH PHP to achieve user personalized search ranking and recommendation.

RISEARCH is a powerful full-text search engine toolkit based on Redis. Redis is an open source, high-performance in-memory database with features such as fast reading and writing, data persistence, and support for complex data types. RISEARCH takes advantage of these features of Redis to implement efficient and flexible full-text search functions.

The following will introduce how to use RISEARCH PHP to achieve user personalized search ranking and recommendation. First, we need to install the Redis and RISEARCH extensions. Installation and configuration can be done through the official website http://redis.io/ and https://github.com/RediSearch/RediSearch-Go/blob/master/README.md.

After the installation is complete, we first create a Redis connection object and a RISEARCH index object:

$redis = new Redis();
$redis->connect('127.0.0.1', 6379);

$index = new RiSearchIndex($redis, 'my_index');

Next, we need to define the fields and weights of the index. These fields will affect the ranking of search results. :

$index->field('title', 2.0, true);
$index->field('content', 1.0, false);

When adding a document to the index, we can set the properties and values ​​of the document, such as user ID, keywords, etc.:

$document = new RiSearchDocument('doc1');
$document->setProperty('user_id', '123');
$document->setProperty('keywords', 'PHP, RiSearch');
$document->addField('title', 'RISEARCH PHP');
$document->addField('content', 'RISEARCH 是一个强大的全文搜索引擎工具包。');

Then add the document to the index:

$index->add($document);

Next, we can use the search method provided by RISEARCH for personalized search. First, we need to create a search query object and specify the keywords:

$query = new RiSearchQuery();
$query->setQueryString('RISEARCH PHP');

If we want personalized search results, we can adjust the weight of the query based on the user's attributes and preferences:

$query->setScorer(function($docId, $docProperties, $score) {
    $userId = $docProperties['user_id'];
    $keywords = $docProperties['keywords'];

    // 根据用户ID和关键词调整权重
    if ($userId == '123') {
        $score *= 2;
    }

    return $score;
});

Finally, we can execute the query and get the search results:

$results = $index->search($query);

In addition to personalized search rankings, RISEARCH also provides recommendation functions based on user preferences. We can recommend relevant content to users based on their previous search records and click behavior.

In RISEARCH, we can use clustering algorithms to implement recommendation functions. Clustering algorithms can group documents into similar categories and then provide personalized recommendations by recommending relevant category content based on the category the user is currently in.

First, we need to create a clustered index object:

$clusterIndex = new RiSearchClusterIndex($redis, 'cluster_index');

Then, we can add documents to the clustered index:

$clusterIndex->add($document);

Next, we can use K-Means clustering algorithm performs clustering operations. Suppose we cluster into 3 categories:

$clusterIndex->cluster(3);

Then, we can recommend relevant content to the user based on the category the user is currently in:

$recommendations = $clusterIndex->recommend('doc1', 5);

The above is to use RISEARCH PHP to achieve user personalization Process and code examples for search ranking and recommendation. By flexibly utilizing the functions of Redis and RISEARCH, we can achieve a search experience that better meets user needs and improve user satisfaction and stickiness.

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