文章目录
- 第 54 、55 天: 基于 M-distance 的推荐
- 代码
- 运行截图
第 54 、55 天: 基于 M-distance 的推荐
1.M-distance, 就是根据平均分来计算两个用户 (或项目) 之间的距离.
2.邻居不用 k 控制. 距离小于 radius (即 ϵ ) 的都是邻居. 使用 M-distance 时, 这种方式效果更好.
3. 使用 leave-one-out 的测试方式,
4. 原本代码是 item-based recommendation.增加了 user-based recommendation.，另造了个构造器。多打了个参数以作区别，方式是将压缩矩阵转置, 用户与项目关系互换.
代码
package machineLearning.knn;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
/**
* ClassName: MBR
* Package: machineLearning.knn
* Description: M-distance.
*
* @Author: luv_x_c
* @Create: 2023/6/14 14:32
*/
public class MBR {
/**
* Default rating for 1-5 points.
*/
public static final double DEFAULT_RATING = 3.0;
/**
* The total number of users.
*/
private int numUsers;
/**
* The total number of items.
*/
private int numItems;
/**
* The total number of ratings.(None zero values)
*/
private int numRatings;
/**
* The predictions.
*/
private double[] predictions;
/**
* Compressed matrix. User-item-rating triples.
*/
private int[][] compressRatingMatrix;
/**
* User-Item Rating Matrix, transposed from the compressRatingMatrix.
* 用户-物品评分矩阵，为 compressRatingMatrix 的转置。
*/
private int[][] userItemRatingMatrix;
/**
* The degree of users.(how many items he has rated).
*/
private int[] userDegrees;
/**
* The average rating of the current user.
*/
private double[] userAverageRatings;
/**
* The degree of items .(How many ratings it has.)
*/
private int[] itemDegrees;
/**
* The average rating of the current item.
*/
private double[] itemAverageRatings;
/**
* The first user start from 0. Let the first user hax x ratings, the second user will start from x.
*/
private int[] userStartingIndices;
/**
* Number of non-neighbor objects.
*/
private int numNoneNeighbors;
/**
* The radius (delta) for determining the neighborhood.
*/
private double radius;
/**
* Construct the rating matrix.
*
* @param paraFilename The rating filename.
* @param paraNumUsers Number of users.
* @param paraNumItems Number of items.
* @param paraNumRatings Number of ratings.
*/
public MBR(String paraFilename, int paraNumUsers, int paraNumItems, int paraNumRatings) throws Exception {
// Step1. Initialize these arrays.
numItems = paraNumItems;
numUsers = paraNumUsers;
numRatings = paraNumRatings;
userDegrees = new int[numUsers];
userStartingIndices = new int[numUsers + 1];
userAverageRatings = new double[numUsers];
itemDegrees = new int[numItems];
compressRatingMatrix = new int[numRatings][3];
itemAverageRatings = new double[numItems];
predictions = new double[numRatings];
System.out.println("Reading " + paraFilename);
// Step2. Read the data file.
File tempFile = new File(paraFilename);
if (!tempFile.exists()) {
System.out.println("File " + paraFilename + " does not exists ");
System.exit(0);
}// Of if
BufferedReader tempBufReader = new BufferedReader(new FileReader(tempFile));
String tempString;
String[] tempStrArray;
int tempIndex = 0;
userStartingIndices[0] = 0;
userStartingIndices[numUsers] = numRatings;
while ((tempString = tempBufReader.readLine()) != null) {
// Each line has three values.
tempStrArray = tempString.split(",");
compressRatingMatrix[tempIndex][0] = Integer.parseInt(tempStrArray[0]);
compressRatingMatrix[tempIndex][1] = Integer.parseInt(tempStrArray[1]);
compressRatingMatrix[tempIndex][2] = Integer.parseInt(tempStrArray[2]);
userDegrees[compressRatingMatrix[tempIndex][0]]++;
itemDegrees[compressRatingMatrix[tempIndex][0]]++;
if (tempIndex > 0) {
// Starting to read the data of a new user.
if (compressRatingMatrix[tempIndex][0] != compressRatingMatrix[tempIndex - 1][0]) {
userStartingIndices[compressRatingMatrix[tempIndex][0]] = tempIndex;
}// OF if
}// Of if
tempIndex++;
}// Of while
tempBufReader.close();
double[] tempUserTotalScore = new double[numUsers];
double[] tempItemTotalScore = new double[numItems];
for (int i = 0; i < numRatings; i++) {
tempUserTotalScore[compressRatingMatrix[i][0]] += compressRatingMatrix[i][2];
tempItemTotalScore[compressRatingMatrix[i][1]] += compressRatingMatrix[i][2];
}// Of for i
for (int i = 0; i < numUsers; i++) {
userAverageRatings[i] = tempUserTotalScore[i] / userDegrees[i];
}// OF for i
for (int i = 0; i < numItems; i++) {
itemAverageRatings[i] = tempItemTotalScore[i] / itemDegrees[i];
}// Of for i
}// OF the first constructor
/**
* Construct the rating matrix and transpose it.
* 构造评分矩阵并进行转置。
*
* @param paraFilename The rating filename.
* @param paraNumUsers Number of users.
* @param paraNumItems Number of items.
* @param paraNumRatings Number of ratings.
*/
public MBR(String paraFilename, int paraNumUsers, int paraNumItems, int paraNumRatings,int paraConstructor) throws Exception {
// Step1. Initialize these arrays.
numItems = paraNumItems;
numUsers = paraNumUsers;
numRatings = paraNumRatings;
userDegrees = new int[numUsers];
userStartingIndices = new int[numUsers + 1];
userAverageRatings = new double[numUsers];
itemDegrees = new int[numItems];
compressRatingMatrix = new int[numRatings][3];
itemAverageRatings = new double[numItems];
predictions = new double[numRatings];
// Step2. Read the data file and construct the userItemRatingMatrix.
System.out.println("Reading " + paraFilename);
userItemRatingMatrix = new int[numItems][numUsers]; // Transposed matrix
File tempFile = new File(paraFilename);
if (!tempFile.exists()) {
System.out.println("File " + paraFilename + " does not exist");
System.exit(0);
}
BufferedReader tempBufReader = new BufferedReader(new FileReader(tempFile));
String tempString;
String[] tempStrArray;
int tempIndex = 0;
userStartingIndices[0] = 0;
userStartingIndices[numUsers] = numRatings;
while ((tempString = tempBufReader.readLine()) != null) {
tempStrArray = tempString.split(",");
int userIndex = Integer.parseInt(tempStrArray[0]);
int itemIndex = Integer.parseInt(tempStrArray[1]);
int rating = Integer.parseInt(tempStrArray[2]);
compressRatingMatrix[tempIndex][0] = userIndex;
compressRatingMatrix[tempIndex][1] = itemIndex;
compressRatingMatrix[tempIndex][2] = rating;
// Transpose and store in the userItemRatingMatrix
userItemRatingMatrix[itemIndex][userIndex] = rating;
userDegrees[userIndex]++;
itemDegrees[itemIndex]++;
if (tempIndex > 0 && compressRatingMatrix[tempIndex][0] != compressRatingMatrix[tempIndex - 1][0]) {
userStartingIndices[compressRatingMatrix[tempIndex][0]] = tempIndex;
}
tempIndex++;
}
tempBufReader.close();
// Calculate average ratings for users and items.
double[] tempUserTotalScore = new double[numUsers];
double[] tempItemTotalScore = new double[numItems];
for (int i = 0; i < numRatings; i++) {
tempUserTotalScore[compressRatingMatrix[i][0]] += compressRatingMatrix[i][2];
tempItemTotalScore[compressRatingMatrix[i][1]] += compressRatingMatrix[i][2];
}
for (int i = 0; i < numUsers; i++) {
userAverageRatings[i] = tempUserTotalScore[i] / userDegrees[i];
}
for (int i = 0; i < numItems; i++) {
itemAverageRatings[i] = tempItemTotalScore[i] / itemDegrees[i];
}
}
/**
* Set the radius.
*
* @param paraRadius The given radius.
*/
public void setRadius(double paraRadius) {
if (paraRadius > 0) {
radius = paraRadius;
} else {
radius = 0.1;
}// OF if
}// Of setRadius
/**
* Leave-one-out prediction. The predicted values are stored in predictions.
*/
public void leaveOneOutPrediction() {
double tempItemAverageRating;
// Make each line of the code shorter.
int tempUser, tempItem, tempRating;
System.out.println("\r\nLeaveOneOutPrediction for radius " + radius);
numNoneNeighbors = 0;
for (int i = 0; i < numRatings; i++) {
tempUser = compressRatingMatrix[i][0];
tempItem = compressRatingMatrix[i][1];
tempRating = compressRatingMatrix[i][2];
// Step1. Recompute average rating of the current item.
tempItemAverageRating =
(itemAverageRatings[tempItem] * itemDegrees[tempItem] - tempRating) / (itemDegrees[tempItem] - 1);
// Step2. Recompute neighbors, at the same time obtain the ratings
// OF neighbors
int tempNeighbors = 0;
double tempTotal = 0;
int tempComparedItem;
for (int j = userStartingIndices[tempUser]; j < userStartingIndices[tempUser + 1]; j++) {
tempComparedItem = compressRatingMatrix[j][1];
if (tempItem == tempComparedItem) {
continue;// Ignore itself
}// Of if
if (Math.abs(tempItemAverageRating - itemAverageRatings[tempComparedItem]) < radius) {
tempTotal += compressRatingMatrix[j][2];
tempNeighbors++;
}// Of if
}// OF for j
//Step3. Predict as the average value of neighbors.
if (tempNeighbors > 0) {
predictions[i] = tempTotal / tempNeighbors;
} else {
predictions[i] = DEFAULT_RATING;
numNoneNeighbors++;
}// Of if
}// OF for i
}// of LeaveOneOutPrediction
/**
* Compute the MAE based on the deviation of each leave-one-out.
*/
public double computeMAE() throws Exception {
double tempTotalError = 0;
for (int i = 0; i < predictions.length; i++) {
tempTotalError += Math.abs(predictions[i] - compressRatingMatrix[i][2]);
}// OF for i
return tempTotalError / predictions.length;
}// OF computeMAE
/**
* ************************
* Compute the MAE based on the deviation of each leave-one-out.
* ************************
*/
public double computeRSME() throws Exception {
double tempTotalError = 0;
for (int i = 0; i < predictions.length; i++) {
tempTotalError += (predictions[i] - compressRatingMatrix[i][2])
* (predictions[i] - compressRatingMatrix[i][2]);
} // Of for i
double tempAverage = tempTotalError / predictions.length;
return Math.sqrt(tempAverage);
}// Of computeRSME
/**
* The entrance of the program.
*
* @param args Not used now.
*/
public static void main(String[] args) {
try {
MBR tempRecommender = new MBR("E:\\java_code\\data\\sampledata\\movielens-943u1682m.txt", 943, 1682,
100000,22);
for (double tempRadius = 0.2; tempRadius < 0.6; tempRadius += 0.1) {
tempRecommender.setRadius(tempRadius);
tempRecommender.leaveOneOutPrediction();
double tempMAE = tempRecommender.computeMAE();
double tempRSME = tempRecommender.computeRSME();
System.out.println("Radius = " + tempRadius + ", MAE = " + tempMAE + ", RSME = " + tempRSME
+ ", numNonNeighbors = " + tempRecommender.numNoneNeighbors);
} // Of for tempRadius
} catch (Exception ee) {
System.out.println(ee);
} // Of try
}// Of main
}// Of class MBR
运行截图
