标签:load tor 防止 关于 使用 random targe ati ++
关于LFM分解理论和python实现见https://www.cnblogs.com/little-horse/p/12489619.html。
以下是java简单实现,完整程序见https://github.com/jiangnanboy/RecomSys/blob/master/src/main/java/com/sy/zhihai/model/LFM.java,数据https://github.com/jiangnanboy/RecomSys/tree/master/src/main/resources
import java.util.Map.Entry; import java.util.Scanner; import javolution.util.FastMap; import javolution.util.FastList; /** * LFM(latent factor model)隐语义推荐模型,矩阵分解,训练得到U,I矩阵 * 对user-item评分矩阵进行分解为U、I矩阵,再利用随机梯度下降(函数值下降最快的方向)迭代求解出U,I矩阵,最后用U*I预测得出user对item的预测评分 * 这里U矩阵是user对每个隐因子的偏好程度,I矩阵是item在每个隐因子中的分布 **/ public class LFM extends AbsMF { public LFM() { } public static void main(String[] args) { String dataPath = "resultData.txt"; LFM lfm = new LFM(); lfm.loadData(dataPath); lfm.initParam(30, 0.02, 0.01, 50); lfm.train(); System.out.println("Input userID..."); Scanner in = new Scanner(System.in); while (true) { String userID = in.nextLine(); FastList<RecommendedItem> recommendedItems = lfm.calRecSingleUser(userID, 50); lfm.displayRecoItem(userID, recommendedItems); System.out.println("Input userID..."); } } /** * 初始化F,α,λ,max_iter,U,I * * @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... " + "F= " + F + "; " + "α= " + α + "; " + "λ= " + λ + "; " + "max_iter= " + max_iter + ";"); 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[]>(); String userID = null; Double[] randomUValue = null; Double[] randomIValue = null; //对U,I矩阵随机初始化 for (Entry<String, FastMap<String, Double>> entry : ratingData.entrySet()) { userID = entry.getKey(); 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 (String itemID : entry.getValue().keySet()) { if (I.containsKey(itemID)) continue; randomIValue = new Double[F]; for (int i = 0; i < F; i++) { double rand = Math.random() / Math.sqrt(F); randomIValue[i] = rand; } I.put(itemID, randomIValue); } } } /** * 随机梯度下降训练U,I矩阵 */ @Override public void train() { System.out.println("training U,I..."); 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()) { String userID = entry.getKey(); for (Entry<String, Double> entry1 : entry.getValue().entrySet()) { String itemID = entry1.getKey(); double pui = this.predictRating(userID, itemID); double err = entry1.getValue() - pui;//根据当前参数计算误差 Double[] userValue = this.U.get(userID); Double[] itemValue = this.I.get(itemID); for (int i = 0; i < this.F; i++) { 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 - this.λ * it); userValue[i] = us; itemValue[i] = it; } } } 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=P*Q * * @param userID * @param itemID * @return */ @Override public double predictRating(String userID, String itemID) { double p = 0.0; Double[] userValue = this.U.get(userID); Double[] itemValue = this.I.get(itemID); for (int i = 0; i < this.F; i++) { p += userValue[i] * itemValue[i]; } return p; } }
标签:load tor 防止 关于 使用 random targe ati ++
原文地址:https://www.cnblogs.com/little-horse/p/12623223.html