标签：itemID java double userID Item 分解 svdpp Double new

融合了偏置LFM以及用户的历史评分行为，结合了邻域和LFM。理论及python实现见https://www.cnblogs.com/little-horse/p/12499671.html。

以下java简单实现，完整程序https://github.com/jiangnanboy/RecomSys/blob/master/src/main/java/com/sy/zhihai/model/SVDPP.java，数据https://github.com/jiangnanboy/RecomSys/tree/master/src/main/resources。

import java.util.Map;
import java.util.Scanner;
import java.util.Map.Entry;

import javolution.util.FastList;
import javolution.util.FastMap;


/**
 * 融合了BiasLFM以及用户的历史评分行为
 * 结合了邻域和LFM
 */
public class SVDPP extends AbsMF {

    private FastMap<String, Double> BU = null;//user的偏置项
    private FastMap<String, Double> BI = null;//item的偏置项
    private FastMap<String, Double[]> Y = null;
    private double sumMean = 0.0;//全体评分的平均

    public SVDPP() {
    }

    public static void main(String[] args) {
        String dataPath = "resultData.txt";
        SVDPP svdpp = new SVDPP();
        svdpp.loadData(dataPath);
        svdpp.initParam(30, 0.02, 0.01, 50);
        svdpp.train();

        System.out.println("Input userID...");
        Scanner in = new Scanner(System.in);
        while (true) {
            String userID = in.nextLine();
            FastList<RecommendedItem> recommendedItems = svdpp.calRecSingleUser(userID, 50);
            svdpp.displayRecoItem(userID, recommendedItems);
            System.out.println("Input userID...");
        }
    }


    /**
     * 初始化F,α,λ,max_iter,U,I,BU,BI,Y
     *
     * @param F        隐因子数目
     * @param α        学习速率
     * @param λ        正则化参数，以防过拟合
     * @param max_iter 迭代次数
     */
    @Override
    public void initParam(int F, double α, double λ, int max_iter) {
        System.out.println("init U,I,BU,BI...");
        this.F = F;
        this.α = α;
        this.λ = λ;
        this.max_iter = max_iter;
        this.U = new FastMap<String, Double[]>();
        this.I = new FastMap<String, Double[]>();
        this.BU = new FastMap<String, Double>();
        this.BI = new FastMap<String, Double>();
        this.Y = new FastMap<String, Double[]>();
        int itemCount = 0;//所有user对有过行为的item总数

        Double[] randomUValue = null;
        Double[] randomIValue = null;
        Double[] randomYValue = null;
        //对U,I,Y矩阵随机初始化
        for (Entry<String, FastMap<String, Double>> entry : ratingData.entrySet()) {
            String userID = entry.getKey();
            this.BU.put(userID, 0.0);
            itemCount += entry.getValue().size();
            randomUValue = new Double[F];
            for (int i = 0; i < F; i++) {
                double rand = Math.random() / Math.sqrt(F);//随机数填充初始化矩阵，并和1/sqrt(F)成正比
                randomUValue[i] = rand;
            }
            U.put(userID, randomUValue);

            for (Entry<String, Double> entryItem : entry.getValue().entrySet()) {
                this.sumMean += entryItem.getValue();
                String itemID = entryItem.getKey();
                this.BI.put(itemID, 0.0);
                if (I.containsKey(itemID))
                    continue;
                randomIValue = new Double[F];
                randomYValue = new Double[F];
                for (int i = 0; i < F; i++) {
                    double randI = Math.random() / Math.sqrt(F);
                    randomIValue[i] = randI;
                    double randY = Math.random() / Math.sqrt(F);
                    randomYValue[i] = randY;
                }
                I.put(itemID, randomIValue);
                Y.put(itemID, randomYValue);
            }
        }
        this.sumMean /= itemCount;
    }

    /**
     * 随机梯度下降训练U,I,BU,BI,Y
     */
    @Override
    public void train() {
        System.out.println("training U,I,BU,BI,Y...");
        for (int step = 0; step < this.max_iter; step++) {
            System.out.println("第" + (step + 1) + "次迭代...");
            for (Entry<String, FastMap<String, Double>> entry : this.ratingData.entrySet()) {
                double[] z_Item = new double[this.F];//此用户历史数据的隐偏好之和
                for (String item : entry.getValue().keySet()) {
                    Double[] Y_Item = this.Y.get(item);
                    for (int f = 0; f < this.F; f++) {
                        z_Item[f] += Y_Item[f];
                    }
                }
                double itemLength_Sqrt = 1.0 / Math.sqrt(1.0 * entry.getValue().size());
                double[] s = new double[this.F];

                String userID = entry.getKey();
                for (Entry<String, Double> itemRatingEntry : entry.getValue().entrySet()) {
                    String itemID = itemRatingEntry.getKey();
                    double pui = this.predictRating(userID, itemID);
                    double err = itemRatingEntry.getValue() - pui;//根据当前参数计算误差(真实值-预测值)
                    double bu = this.BU.get(userID);
                    bu += this.α * (err - this.λ * bu);
                    this.BU.put(userID, bu);
                    double bi = this.BI.get(itemID);
                    bi += this.α * (err - this.λ * bi);
                    this.BI.put(itemID, bi);
                    Double[] userValue = this.U.get(userID);
                    Double[] itemValue = this.I.get(itemID);
                    for (int i = 0; i < this.F; i++) {
                        s[i] += itemValue[i] * err;
                        double us = userValue[i];
                        double it = itemValue[i];
                        us += this.α * (err * it - this.λ * us);//后一项是来防止过拟合的正则化项，λ需要根据具体应用场景反复实验得到。损失函数的优化使用随机梯度下降算法
                        it += this.α * (err * (us + z_Item[i] * itemLength_Sqrt) - this.λ * it);
                        userValue[i] = us;
                        itemValue[i] = it;
                    }
                }

                for (String item : entry.getValue().keySet()) {
                    Double[] Y_Item = this.Y.get(item);
                    for (int f = 0; f < this.F; f++) {
                        double y = Y_Item[f];
                        y += this.α * (s[f] * itemLength_Sqrt - this.λ * y);
                        Y_Item[f] = y;
                    }
                }
            }
            this.α *= 0.9;//每次迭代步长要逐步缩小
        }
    }

    /**
     * userID对itemID的评分
     * U每行表示该用户对各个隐因子的偏好程度
     * I每列表示该物品在各个隐患因子中的概率分布
     * rating=(U+Y/sqrt(sum(ui)))*I+sumMean+BU+BI
     *
     * @param userID
     * @param itemID
     * @return
     */
    @Override
    public double predictRating(String userID, String itemID) {
        double[] z_Item = new double[this.F];
        Map<String, Double> ratingItem = this.ratingData.get(userID);
        for (String item : ratingItem.keySet()) {
            Double[] Y_Item = this.Y.get(item);
            for (int f = 0; f < this.F; f++)
                z_Item[f] += Y_Item[f];
        }
        double p = 0.0;
        Double[] userValue = this.U.get(userID);
        Double[] itemValue = this.I.get(itemID);
        for (int i = 0; i < this.F; i++) {
            double rating = userValue[i] + z_Item[i] / Math.sqrt(1.0 * ratingItem.size());
            p += rating * itemValue[i];
        }
        p += this.BU.get(userID) + this.BI.get(itemID) + this.sumMean;
        return p;
    }

}

标签：itemID,java,double,userID,Item,分解,svdpp,Double,new
来源： https://www.cnblogs.com/little-horse/p/12623300.html

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。