방문 조리 시스템의 바둑 언어 개발: 메뉴 추천 기능을 구현하는 방법은 무엇입니까?

package main

import (
    "fmt"
    "net/http"
    "io/ioutil"
)

func main() {
    url := "https://www.example.com/recipes"
    
    resp, err := http.Get(url)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    defer resp.Body.Close()
    
    body, err := ioutil.ReadAll(resp.Body)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    fmt.Println(string(body))
}

package main

import (
    "fmt"
    "github.com/jinzhu/gorm"
    _ "github.com/jinzhu/gorm/dialects/mysql"
)

type User struct {
    gorm.Model
    Name    string
    Age     int
    Orders  []Order
}

type Order struct {
    gorm.Model
    UserID    uint
    MenuName  string
}

func main() {
    db, err := gorm.Open("mysql", "user:password@/dbname?charset=utf8&parseTime=True&loc=Local")
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    defer db.Close()

    // 创建表
    db.AutoMigrate(&User{})
    db.AutoMigrate(&Order{})

    // 存储用户信息和订餐记录
    user := User{Name: "Tom", Age: 25}
    order1 := Order{UserID: user.ID, MenuName: "宫保鸡丁"}
    order2 := Order{UserID: user.ID, MenuName: "鱼香肉丝"}

    db.Create(&user)
    db.Create(&order1)
    db.Create(&order2)
    
    // 查询用户的订餐记录
    var orders []Order
    db.Model(&user).Related(&orders)

    fmt.Println(user)
    fmt.Println(orders)
}

package main

import (
    "fmt"
    "github.com/sjwhitworth/golearn/base"
    "github.com/sjwhitworth/golearn/evaluation"
    "github.com/sjwhitworth/golearn/filters"
    "github.com/sjwhitworth/golearn/trees"
)

func main() {
    // 构建数据集
    rawData, err := base.ParseCSVToInstances("menu.csv", false)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    // 使用推荐算法对数据集进行训练和评估
    trainData, testData := base.InstancesTrainTestSplit(rawData, 0.7)
    
    tree := trees.NewID3DecisionTree(0.6)

    // 使用协同过滤算法进行训练
    filter := filters.NewChiMergeFilter(trainData, 0.999)
    trainDataFiltered := base.NewLazilyFilteredInstances(trainData, filter)
    
    tree.Fit(trainDataFiltered)
    
    // 对测试数据进行预测
    predictions, err := tree.Predict(testData)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    // 计算准确率
    confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    accuracy := evaluation.GetAccuracy(confusionMat)
    fmt.Println("Accuracy: ", accuracy)
}