如何提高C++大数据开发中的数据推荐效果?

#include <iostream>
#include <vector>

// 定义用户物品矩阵
std::vector<std::vector<int>> userItemMatrix = {
    {5, 3, 0, 1},
    {4, 0, 0, 1},
    {1, 1, 0, 5},
    {1, 0, 0, 4},
    {0, 1, 5, 4}
};

// 计算欧氏距离
double euclideanDistance(const std::vector<int>& vec1, const std::vector<int>& vec2) {
    double sum = 0.0;
    for (size_t i = 0; i < vec1.size(); ++i) {
        sum += (vec1[i] - vec2[i]) * (vec1[i] - vec2[i]);
    }
    return sqrt(sum);
}

// 计算相似度矩阵
std::vector<std::vector<double>> calculateSimilarityMatrix() {
    std::vector<std::vector<double>> similarityMatrix(userItemMatrix.size(), std::vector<double>(userItemMatrix.size(), 0.0));
    for (size_t i = 0; i < userItemMatrix.size(); ++i) {
        for (size_t j = 0; j < userItemMatrix.size(); ++j) {
            if (i != j) {
                double distance = euclideanDistance(userItemMatrix[i], userItemMatrix[j]);
                similarityMatrix[i][j] = 1 / (1 + distance);
            }
        }
    }
    return similarityMatrix;
}

int main() {
    std::vector<std::vector<double>> similarityMatrix = calculateSimilarityMatrix();
    // 输出相似度矩阵
    for (size_t i = 0; i < similarityMatrix.size(); ++i) {
        for (size_t j = 0; j < similarityMatrix[i].size(); ++j) {
            std::cout << similarityMatrix[i][j] << " ";
        }
        std::cout << std::endl;
    }
    return 0;
}