C++聚類算法在推薦系統冷啟動問題中的解決策略

在推薦系統中，冷啟動問題是指系統在新啟動時，由于缺乏用戶歷史數據和物品信息，難以進行有效的推薦。C++聚類算法可以在一定程度上解決冷啟動問題，通過將新用戶或新物品聚類，發現潛在的興趣相似性，從而進行推薦。以下是幾種使用C++聚類算法解決推薦系統冷啟動問題的策略：

1. 用戶聚類

通過將新用戶聚類，可以發現具有相似興趣的用戶群體，從而推薦這些用戶喜歡的物品。常用的聚類算法包括K-means、DBSCAN等。

#include <iostream>
#include <vector>
#include <cmath>
#include <kmeans.h> // 假設使用了一個C++的K-means庫

using namespace std;

// 用戶特征向量
struct UserFeature {
    int userId;
    vector<double> features;
};

// K-means聚類
vector<vector<UserFeature>> kmeansClustering(const vector<UserFeature>& users, int k) {
    // 初始化質心
    vector<UserFeature> centroids(k);
    for (int i = 0; i < k; ++i) {
        centroids[i] = users[i];
    }

    // 迭代過程
    bool converged = false;
    while (!converged) {
        vector<vector<UserFeature>> clusters(k);
        vector<int> cluster assignments(users.size(), -1);

        for (const auto& user : users) {
            double minDist = DBL_MAX;
            int closestCluster = -1;
            for (int i = 0; i < k; ++i) {
                double dist = euclideanDistance(user, centroids[i]);
                if (dist < minDist) {
                    minDist = dist;
                    closestCluster = i;
                }
            }
            clusters[closestCluster].push_back(user);
            clusterAssignments[user.userId] = closestCluster;
        }

        // 更新質心
        vector<UserFeature> newCentroids(k);
        for (int i = 0; i < k; ++i) {
            vector<double> sumFeatures(users[0].features.size(), 0.0);
            int count = 0;
            for (const auto& user : clusters[i]) {
                for (size_t j = 0; j < user.features.size(); ++j) {
                    sumFeatures[j] += user.features[j];
                }
                count++;
            }
            for (size_t j = 0; j < sumFeatures.size(); ++j) {
                newCentroids[i].features[j] = sumFeatures[j] / count;
            }
        }

        if (centroids == newCentroids) {
            converged = true;
        } else {
            centroids = newCentroids;
        }
    }

    return clusters;
}

// 計算歐幾里得距離
double euclideanDistance(const UserFeature& a, const UserFeature& b) {
    double sum = 0.0;
    for (size_t i = 0; i < a.features.size(); ++i) {
        sum += pow(a.features[i] - b.features[i], 2);
    }
    return sqrt(sum);
}

int main() {
    vector<UserFeature> users = {
        {1, {1.0, 2.0, 3.0}},
        {2, {4.0, 5.0, 6.0}},
        {3, {7.0, 8.0, 9.0}},
        {4, {10.0, 11.0, 12.0}}
    };

    int k = 2;
    vector<vector<UserFeature>> clusters = kmeansClustering(users, k);

    for (const auto& cluster : clusters) {
        cout << "Cluster:" << endl;
        for (const auto& user : cluster) {
            cout << "User ID: " << user.userId << ", Features: ";
            for (double feature : user.features) {
                cout << feature << " ";
            }
            cout << endl;
        }
    }

    return 0;
}

2. 物品聚類

通過將新物品聚類，可以發現具有相似特征的物品群體，從而推薦這些物品給相似用戶。常用的聚類算法同樣包括K-means、DBSCAN等。

#include <iostream>
#include <vector>
#include <cmath>
#include <kmeans.h> // 假設使用了一個C++的K-means庫

using namespace std;

// 物品特征向量
struct ItemFeature {
    int itemId;
    vector<double> features;
};

// K-means聚類
vector<vector<ItemFeature>> kmeansClustering(const vector<ItemFeature>& items, int k) {
    // 初始化質心
    vector<ItemFeature> centroids(k);
    for (int i = 0; i < k; ++i) {
        centroids[i] = items[i];
    }

    // 迭代過程
    bool converged = false;
    while (!converged) {
        vector<vector<ItemFeature>> clusters(k);
        vector<int> clusterAssignments(items.size(), -1);

        for (const auto& item : items) {
            double minDist = DBL_MAX;
            int closestCluster = -1;
            for (int i = 0; i < k; ++i) {
                double dist = euclideanDistance(item, centroids[i]);
                if (dist < minDist) {
                    minDist = dist;
                    closestCluster = i;
                }
            }
            clusters[closestCluster].push_back(item);
            clusterAssignments[item.itemId] = closestCluster;
        }

        // 更新質心
        vector<ItemFeature> newCentroids(k);
        for (int i = 0; i < k; ++i) {
            vector<double> sumFeatures(items[0].features.size(), 0.0);
            int count = 0;
            for (const auto& item : clusters[i]) {
                for (size_t j = 0; j < item.features.size(); ++j) {
                    sumFeatures[j] += item.features[j];
                }
                count++;
            }
            for (size_t j = 0; j < sumFeatures.size(); ++j) {
                newCentroids[i].features[j] = sumFeatures[j] / count;
            }
        }

        if (centroids == newCentroids) {
            converged = true;
        } else {
            centroids = newCentroids;
        }
    }

    return clusters;
}

// 計算歐幾里得距離
double euclideanDistance(const ItemFeature& a, const ItemFeature& b) {
    double sum = 0.0;
    for (size_t i = 0; i < a.features.size(); ++i) {
        sum += pow(a.features[i] - b.features[i], 2);
    }
    return sqrt(sum);
}

int main() {
    vector<ItemFeature> items = {
        {1, {1.0, 2.0, 3.0}},
        {2, {4.0, 5.0, 6.0}},
        {3, {7.0, 8.0, 9.0}},
        {4, {10.0, 11.0, 12.0}}
    };

    int k = 2;
    vector<vector<ItemFeature>> clusters = kmeansClustering(items, k);

    for (const auto& cluster : clusters) {
        cout << "Cluster:" << endl;
        for (const auto& item : cluster) {
            cout << "Item ID: " << item.itemId << ", Features: ";
            for (double feature : item.features) {
                cout << feature << " ";
            }
            cout << endl;
        }
    }

    return 0;
}

3. 基于聚類的推薦策略

在聚類完成后，可以根據聚類結果進行推薦。例如，對于新用戶，可以推薦其所在聚類中的熱門物品；對于新物品，可以推薦其所在聚類中的用戶喜歡的物品。

#include <iostream>
#include <vector>
#include <unordered_map>
#include <algorithm>

using namespace std;

// 物品特征向量
struct ItemFeature {
    int itemId;
    vector<double> features;
};

// K-means聚類
vector<vector<ItemFeature>> kmeansClustering(const vector<ItemFeature>& items, int k) {
    // 初始化質心
    vector<ItemFeature> centroids(k);
    for (int i = 0; i < k; ++i) {
        centroids[i] = items[i];
    }

    // 迭代過程
    bool converged = false;
    while (!converged) {
        vector<vector<ItemFeature>> clusters(k);
        vector<int> clusterAssignments(items.size(), -1);

        for (const auto& item : items) {
            double minDist = DBL_MAX;
            int closestCluster = -1;
            for (int i = 0; i < k; ++i) {
                double dist = euclideanDistance(item, centroids[i]);
                if (dist < minDist) {
                    minDist = dist;
                    closestCluster = i;
                }
            }
            clusters[closestCluster].push_back(item);
            clusterAssignments[item.itemId] = closestCluster;
        }

        // 更新質心
        vector<ItemFeature> newCentroids(k);
        for (int i = 0; i < k; ++i) {
            vector<double> sumFeatures(items[0].features.size(), 0.0);
            int count = 0;
            for (const auto& item : clusters[i]) {
                for (size_t j = 0; j < item.features.size(); ++j) {
                    sumFeatures[j] += item.features[j];
                }
                count++;
            }
            for (size_t j = 0; j < sumFeatures.size(); ++j) {
                newCentroids[i].features[j] = sumFeatures[j] / count;
            }
        }

        if (centroids == newCentroids) {
            converged = true;
        } else {
            centroids = newCentroids;
        }
    }

    return clusters;
}

// 計算歐幾里得距離
double euclideanDistance(const ItemFeature& a, const ItemFeature& b) {
    double sum = 0.0;
    for (size_t i = 0; i < a.features.size(); ++i) {
        sum += pow(a.features[i] - b.features[i], 2);
    }
    return sqrt(sum);
}

// 基于聚類的推薦策略
vector<int> recommendItems(const vector<ItemFeature>& items, const vector<vector<ItemFeature>>& clusters, int userId) {
    unordered_map<int, int> userClusterMap;
    for (int i = 0; i < clusters.size(); ++i) {
        for (const auto& item : clusters[i]) {
            userClusterMap[item.itemId] = i;
        }
    }

    vector<int> recommendedItems;
    if (userClusterMap.find(userId) != userClusterMap.end()) {
        const auto& cluster = clusters[userClusterMap[userId]];
        vector<int> itemCounts(items.size(), 0);
        for (const auto& item : cluster) {
            itemCounts[item.itemId]++;
        }

        // 找到最受歡迎的物品
        int maxCount = 0;
        for (int count : itemCounts) {
            if (count > maxCount) {
                maxCount = count;
            }
        }

        // 推薦最受歡迎的物品
        for (int i = 0; i < itemCounts.size(); ++i) {
            if (itemCounts[i] == maxCount) {
                recommendedItems.push_back(i);
            }
        }
    }

    return recommendedItems;
}

int main() {
    vector<ItemFeature> items = {
        {1, {1.0, 2.0, 3.0}},
        {2, {4.0, 5.0, 6.0}},
        {3, {7.0, 8.0, 9.0}},
        {4, {10.0, 11.0, 12.0}}
    };

    int k = 2;
    vector<vector<ItemFeature>> clusters = kmeansClustering(items, k);

    // 假設用戶ID為1
    int userId = 1;
    vector<int> recommendedItems = recommendItems(items, clusters, userId);

    cout << "Recommended items for user " << userId << ": ";
    for (int itemId : recommendedItems) {
        cout << itemId << " ";
    }
    cout << endl;

    return 0;
}

通過以上策略，C++聚類算法可以在推薦系統中有效解決冷啟動問題，提高推薦的準確性和用戶滿意度。