import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.util.ArrayList;
import java.util.List;

public class DataPreprocessing {
    private static final String DB_URL = "jdbc:mysql://localhost:3306/ticketdb";
    private static final String USER = "username";
    private static final String PASS = "password";

    public static List<UserData> fetchData() {
        List<UserData> dataList = new ArrayList<>();
        try (Connection connection = DriverManager.getConnection(DB_URL, USER, PASS)) {
            String query = "SELECT user_id, travel_time, travel_route FROM user_history";
            PreparedStatement preparedStatement = connection.prepareStatement(query);
            ResultSet resultSet = preparedStatement.executeQuery();

            while (resultSet.next()) {
                dataList.add(new UserData(resultSet.getInt("user_id"), resultSet.getTimestamp("travel_time"), resultSet.getString("travel_route")));
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
        return dataList;
    }
}

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import cosine_similarity

# 加載數(shù)據(jù)
data = pd.read_csv('user_data.csv')

# 數(shù)據(jù)預(yù)處理
# 將出行路線轉(zhuǎn)為數(shù)值向量
routes = pd.get_dummies(data['travel_route'])

# 計(jì)算用戶之間的相似度
user_similarity = cosine_similarity(routes)

# 根據(jù)相似度推薦
def recommend(user_id, user_similarity):
    similar_users = user_similarity[user_id].argsort()[-5:][::-1]
    recommendations = data[data['user_id'].isin(similar_users)]
    return recommendations

# 示例測(cè)試
user_id = 1
recommendations = recommend(user_id, user_similarity)
print(recommendations)

import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import org.tensorflow.Tensor;

@RestController
public class RecommendationController {

    private SavedModelBundle model;

    public RecommendationController() {
        // 加載模型
        this.model = SavedModelBundle.load("/path/to/saved/model");
    }

    @GetMapping("/recommend")
    public List<String> recommend(@RequestParam int userId) {
        // 獲取用戶歷史數(shù)據(jù)
        List<UserData> userDataList = DataPreprocessing.fetchDataByUserId(userId);

        // 構(gòu)建輸入張量
        Tensor<String> inputTensor = Tensor.create(userDataList);

        // 進(jìn)行預(yù)測(cè)
        Session session = model.session();
        List<Tensor<?>> outputs = session.runner().feed("input", inputTensor).fetch("output").run();
        Tensor<String> outputTensor = outputs.get(0).expect(String.class);
        
        // 解析結(jié)果
        List<String> recommendations = new ArrayList<>();
        try (outputTensor) {
            recommendations = outputTensor.copyTo(new String[1])[0];
        }
        return recommendations;
    }
}