所谓 M-distance, 就是根据平均分来计算两个用户 (或项目) 之间的距离.
初看还是不太懂，经过老师讲解论文里面的实例，现在也比较清晰了对于程序内容。
当然代码里的各种成员变量啊，方法啊也是不少。
public MBR(String paraFilename, int paraNumUsers, int paraNumItems, int paraNumRatings) throws Exception {
// Step 1. 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];
compressedRatingMatrix = new int[numRatings][3];
itemAverageRatings = new double[numItems];
predictions = new double[numRatings];
System.out.println("Reading " + paraFilename);
// Step 2. 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(",");
compressedRatingMatrix[tempIndex][0] = Integer.parseInt(tempStrArray[0]);
compressedRatingMatrix[tempIndex][1] = Integer.parseInt(tempStrArray[1]);
compressedRatingMatrix[tempIndex][2] = Integer.parseInt(tempStrArray[2]);
userDegrees[compressedRatingMatrix[tempIndex][0]]++;
itemDegrees[compressedRatingMatrix[tempIndex][1]]++;
if (tempIndex > 0) {
// Starting to read the data of a new user.
if (compressedRatingMatrix[tempIndex][0] != compressedRatingMatrix[tempIndex - 1][0]) {
userStartingIndices[compressedRatingMatrix[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[compressedRatingMatrix[i][0]] += compressedRatingMatrix[i][2];
tempItemTotalScore[compressedRatingMatrix[i][1]] += compressedRatingMatrix[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
第一个构造器的内容还是比较多，先是传入各种参数，新建各种一维，二维数组。按行依次读入，一列一列的存储，统计了userDegrees和itemDegrees的数量，并且还考虑到userStartingIndices[]这个数组。最后还分别算出了平均值。
这里我们再来看计算MAE,RSME：
public double computeMAE() throws Exception {
double tempTotalError = 0;
for (int i = 0; i < predictions.length; i++) {
tempTotalError += Math.abs(predictions[i] - compressedRatingMatrix[i][2]);
} // Of for i
return tempTotalError / predictions.length;
}// Of computeMAE
public double computeRSME() throws Exception {
double tempTotalError = 0;
for (int i = 0; i < predictions.length; i++) {
tempTotalError += (predictions[i] - compressedRatingMatrix[i][2])
* (predictions[i] - compressedRatingMatrix[i][2]);
} // Of for i
double tempAverage = tempTotalError / predictions.length;
return Math.sqrt(tempAverage);
}// Of computeRSME
其中computeMAE()算的是绝对距离的平均值，computeRSME()就更像欧式距离，是差距平方和的平均值，最后还开根号了。其中这个predictions.length的大小前面有给出，predictions = new double[numRatings];看主函数传入的参数也能知道就是说评价值为0 的没有计入。
最后就是重中之重，leaveOneOutPrediction()的内容：
public void leaveOneOutPrediction() {
double tempItemAverageRating;
// Make each line of the code shorter.
int tempUser, tempItem, tempRating;
System.out.println("rnLeaveOneOutPrediction for radius " + radius);
numNonNeighbors = 0;
for (int i = 0; i < numRatings; i++) {
tempUser = compressedRatingMatrix[i][0];
tempItem = compressedRatingMatrix[i][1];
tempRating = compressedRatingMatrix[i][2];
// Step 1. Recompute average rating of the current item.
tempItemAverageRating = (itemAverageRatings[tempItem] * itemDegrees[tempItem] - tempRating)
/ (itemDegrees[tempItem] - 1);
// Step 2. 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 = compressedRatingMatrix[j][1];
if (tempItem == tempComparedItem) {
continue;// Ignore itself.
} // Of if
if (Math.abs(tempItemAverageRating - itemAverageRatings[tempComparedItem]) < radius) {
tempTotal += compressedRatingMatrix[j][2];
tempNeighbors++;
} // Of if
} // Of for j
// Step 3. Predict as the average value of neighbors.
if (tempNeighbors > 0) {
predictions[i] = tempTotal / tempNeighbors;
} else {
predictions[i] = DEFAULT_RATING;
numNonNeighbors++;
} // Of if
} // Of for i
}// Of leaveOneOutPrediction
首先要明确的是其中的radius,也是自己设置的。因为是要leave one out,抹去其中一个，所以有tempItemAverageRating = (itemAverageRatings[tempItem] * itemDegrees[tempItem] - tempRating) / (itemDegrees[tempItem] - 1);用之前算得的总共的平均值乘以数量减去需要抹去的那个，得出的结果再除以数量减一。因为我们算是算一行的所以循环里的条件是(int j = userStartingIndices[tempUser]; j < userStartingIndices[tempUser + 1]; j++)；遇到自己就跳过continue,之后就是确定邻居数量，累计tempTotal,算出均值。
最后贴上运行结果：
这种和Knn的区别也很明显，对于邻居的把控，Knn是自己控制自己选择，而这则是基于一个辐射半径，在这范围里的都被视作邻居。
