java实现矩阵分解推荐算法的代码

以下是一个 Java 实现的矩阵分解推荐算法代码示例：

import java.util.*;
import java.io.*;

public class MatrixFactorization {
    int numUsers;
    int numItems;
    double[][] userFactors;
    double[][] itemFactors;
    double learningRate;
    double regularization;
    int numIterations;

    public MatrixFactorization(int numUsers, int numItems, int numFactors,
            double learningRate, double regularization, int numIterations) {
        this.numUsers = numUsers;
        this.numItems = numItems;
        userFactors = new double[numUsers][numFactors];
        itemFactors = new double[numItems][numFactors];
        this.learningRate = learningRate;
        this.regularization = regularization;
        this.numIterations = numIterations;
    }

    public void train(int[][] ratings) {
        for (int iter = 0; iter < numIterations; iter++) {
            for (int u = 0; u < numUsers; u++) {
                for (int i = 0; i < numItems; i++) {
                    if (ratings[u][i] > 0) {
                        double prediction = 0;
                        for (int f = 0; f < userFactors[u].length; f++) {
                            prediction += userFactors[u][f] * itemFactors[i][f];
                        }
                        double error = ratings[u][i] - prediction;
                        for (int f = 0; f < userFactors[u].length; f++) {
                            userFactors[u][f] += learningRate * (error * itemFactors[i][f] - regularization * userFactors[u][f]);
                            itemFactors[i][f] += learningRate * (error * userFactors[u][f] - regularization * itemFactors[i][f]);
                        }
                    }
                }
            }
        }
    }

    public double[][] getUserFactors() {
        return userFactors;
    }

    public double[][] getItemFactors() {
        return itemFactors;
    }

    public static void main(String[] args) {
        int numUsers = 5;
        int numItems = 4;
        int[][] ratings = {{5, 3, 0, 1}, 
                           {4, 0, 0, 1}, 
                           {1, 1, 0, 5}, 
                           {0, 0, 4, 4}, 
                           {0, 1, 5, 4}};

        int numFactors = 2;
        double learningRate = 0.01;
        double regularization = 0.1;
        int numIterations = 100;

        MatrixFactorization mf = new MatrixFactorization(numUsers, numItems, numFactors, learningRate, regularization, numIterations);
        mf.train(ratings);

        double[][] userFactors = mf.getUserFactors();
        double[][] itemFactors = mf.getItemFactors();

        System.out.println("User factors:");
        for (int u = 0; u < numUsers; u++) {
            System.out.print("User " + u + ": ");
            for (int f = 0; f < numFactors; f++) {
                System.out.print(userFactors[u][f] + " ");
            }
            System.out.println();
        }

        System.out.println("Item factors:");
        for (int i = 0; i < numItems; i++) {
            System.out.print("Item " + i + ": ");
            for (int f = 0; f < numFactors; f++) {
                System.out.print(itemFactors[i][f] + " ");
            }
            System.out.println();
        }
    }
}

该代码实现了基于梯度下降的矩阵分解推荐算法。在本例中，我们使用一个大小为 5 x 4 的评分矩阵作为输入，其中每行代表一个用户，每列代表一个物品。为了简化问题，使用了隐含因子数量为 2 的模型。在模型训练期间，每次迭代都会使用随机梯度下降更新用户矩阵和物品矩阵的值，以尝试最小化预测值与实际值之间的误差。在训练完成后，输出用户因子矩阵和物品因子矩阵的值，以便进行推荐。