The second-hand recycling website developed by PHP realizes the user browsing route recommendation function

Introduction:
With the rapid development of the second-hand recycling industry, more and more people are paying attention to the recycling and reuse of second-hand items. . In order to facilitate users to find suitable recycling sites and improve user experience, we decided to add a user browsing route recommendation function to the second-hand recycling website. This article will introduce how to develop this function using PHP, with code examples.

1. Demand analysis:
According to demand analysis, we need to implement the following functions:

1. Automatically recommend nearby recycling sites based on the current location provided by the user;
2. Recommend the next site based on the site records the user has browsed;
3. Recommend sites of interest based on the user's browsing habits.

2. Technology selection:
Because PHP has a wide range of development groups and rich class libraries, we choose PHP as the development language. In order to facilitate development, we use MySQL as the database management system.

3. Implementation steps:

1. Create database:

First, we need to create a database to store recycling site information and user browsing records .

CREATE DATABASE recycle_db;
USE recycle_db;
CREATE TABLE sites (
    id INT PRIMARY KEY AUTO_INCREMENT,
    name VARCHAR(50) NOT NULL,
    address VARCHAR(100) NOT NULL,
    latitude DOUBLE NOT NULL,
    longitude DOUBLE NOT NULL
);
CREATE TABLE user_history (
    id INT PRIMARY KEY AUTO_INCREMENT,
    user_id INT NOT NULL,
    site_id INT NOT NULL,
    FOREIGN KEY (user_id) REFERENCES users(id),
    FOREIGN KEY (site_id) REFERENCES sites(id)
);

2. Get the user's current location:

Use the Geolocation API to get the user's current location information. In the front-end page, the user's latitude and longitude information is obtained through JavaScript code and passed to the PHP back-end.

navigator.geolocation.getCurrentPosition(function(position) {
    const lat = position.coords.latitude;
    const lon = position.coords.longitude;
    // 將經(jīng)緯度信息發(fā)送給后端
    $.post("recommend.php", {lat: lat, lon: lon}, function(data) {
        // 處理后端返回的推薦結(jié)果
        console.log(data);
    });
});

3. Recommend nearby recycling sites based on the user's current location:

In the back-end PHP code, query the database and calculate the user's current location based on the latitude and longitude information provided by the user distance to the recycling site, and then returns recommended results in ascending order of distance.

<?php
$lat = $_POST["lat"];
$lon = $_POST["lon"];
// 連接數(shù)據(jù)庫
$conn = mysqli_connect("localhost", "username", "password", "recycle_db");
$query = "SELECT * FROM sites ORDER BY SQRT(POW(latitude-$lat,2)+POW(longitude-$lon,2))";
$result = mysqli_query($conn, $query);
// 處理查詢結(jié)果
$data = [];
while ($row = mysqli_fetch_assoc($result)) {
    $data[] = $row;
}
// 返回結(jié)果
echo json_encode($data);
?>

4. Recommend the next site based on the user's browsing history:

We can use the user's browsing history to recommend the next site. First, query the site IDs that the user has browsed from the user_history table based on the user's ID. Then, the sites are sorted according to the number of visits, and sites with more visits are recommended.

$query = "SELECT site_id FROM user_history WHERE user_id=$user_id";
$result = mysqli_query($conn, $query);
$visited_sites = [];
while ($row = mysqli_fetch_assoc($result)) {
    $visited_sites[] = $row["site_id"];
}
// 根據(jù)站點(diǎn)訪問次數(shù)進(jìn)行排序
$query = "SELECT site_id, COUNT(*) AS visit_count FROM user_history WHERE site_id NOT IN (" . implode(",", $visited_sites) . ") GROUP BY site_id ORDER BY visit_count DESC";
$result = mysqli_query($conn, $query);
// 處理查詢結(jié)果
$data = mysqli_fetch_assoc($result);
$next_site_id = $data["site_id"];

5. Recommend sites of interest to users based on their browsing habits:

We can use collaborative filtering algorithms to recommend sites of interest to users. The algorithm is based on similarities between users and helps make recommendations by leveraging the historical behavior of other users.

The specific implementation process is briefly mentioned. It is recommended to use open source machine learning libraries such as TensorFlow or scikit-learn to implement the recommendation algorithm.

Conclusion:
By implementing the above functions, we have successfully added the user browsing route recommendation function to the second-hand recycling website. Users can easily find nearby recycling sites and get personalized recommendations based on their location, browsing history and interests. This not only improves the user experience, but also promotes the recycling and reuse of second-hand items.

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