Build a news recommendation engine based on PHP and coreseek

Building a news recommendation engine based on PHP and coreseek

Introduction:
With the rapid development of the Internet, the way people obtain information on a daily basis is also changing. How to quickly and accurately help users filter out news content that matches their interests has become an important challenge. In this article, we will introduce how to use PHP and coreseek to build a news recommendation engine based on keyword matching.

1. Engine architecture

The architecture of the news recommendation engine is shown in the figure below:

User--> Recommendation engine--> coreseek -- > News database

Users submit news keywords through the recommendation engine. The recommendation engine will pass the keywords to coreseek. Coreseek queries the matching news through the index database and returns it to the recommendation engine. The recommendation engine sorts and filters the returned news list and returns the results to the user.

2. Installing and configuring coreseek

First, we need to install and configure coreseek. coreseek is a Chinese full-text indexing tool based on the open source search engine Sphinx, which can be used for fast text retrieval. In the Linux environment, we can install coreseek through the following command:

wget http://www.coreseek.cn/uploads/csft/4.1/coreseek-4.1-beta.tar.gz tar -zxvf coreseek-4.1-beta.tar.gz cd coreseek-4.1-beta ./configure --prefix=/usr/local/coreseek make && make install cd /usr/local/coreseek cp -r /usr/local/coreseek/mmseg-3.2.14/etc/* ./etc/ vi etc/csft.conf

In thecsft.confconfiguration file, we need to set the connection information of the news database, such as host name, port number, etc. .

3. Database and data import

Next, we need to create a news database and import news data. Assuming that we use MySQL as the database management system, we can create the database and tables through the following commands:

CREATE DATABASE news; USE news; CREATE TABLE news ( id INT PRIMARY KEY AUTO_INCREMENT, title VARCHAR(255), content TEXT );

Then, import the news data into the database:

INSERT INTO news (title, content) VALUES ('新闻标题1', '新闻内容1'); INSERT INTO news (title, content) VALUES ('新闻标题2', '新闻内容2'); ...

After importing all the news data into the database, we need Set coreseek's index configuration fileetc/sphinx.conf：

source news { type = mysql sql_host = localhost sql_user = your_mysql_user sql_pass = your_mysql_password sql_db = news sql_port = 3306 sql_query = SELECT id, title, content FROM news } index news_index { source = news path = /usr/local/coreseek/var/data/news_index docinfo = extern mlock = 0 }

4. PHP code example

The following is a simple PHP code example, using To submit user keywords and obtain news recommendation results:

SetServer('localhost', 9312); $sphinx->SetMatchMode(SPH_MATCH_ALL); $sphinx->SetLimits(0, 10); $result = $sphinx->Query($keyword, 'news_index'); if ($result === false) { echo "查询失败"; } else { $ids = array_keys($result['matches']); $news = []; $pdo = new PDO('mysql:host=localhost;dbname=news', 'your_mysql_user', 'your_mysql_password'); $stmt = $pdo->prepare("SELECT title, content FROM news WHERE id IN (" . implode(',', $ids) . ")"); $stmt->execute(); while ($row = $stmt->fetch(PDO::FETCH_ASSOC)) { $news[] = $row; } echo json_encode($news); } ?>

In this example, we use the SphinxClient class provided by the sphinxapi extension library to query with coreseek. First, we set the host name and port number of coreseek through theSetServermethod, then use theSetMatchModemethod to set the matching mode (here is all matching), and finally use theQuerymethod Submit user keywords for query.

If the query is successful, we can obtain the matching news id list through$result['matches'], and then use the PDO class to interact with MySQL to query the corresponding news title based on the id and content.

5. Conclusion

Through the above steps, we successfully built a news recommendation engine based on PHP and coreseek. You can perform secondary development according to your own needs, such as adding functions such as user login and personalized recommendations. I hope this article helps you build a news recommendation engine!

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