个性化服务推荐

for (vector<double>::size_type i = 0; i < col; ++i)
{
for (vector<double>::size_type j = 0; j < col; ++j)
{
double degree = SumVector<double>(transData[j]);
for (vector<vector<T> >::size_type i = 0; i < row; ++i)
{
for (vector<T>::size_type j = 0; j < col; ++j)
{
output << setprecision(3)<< Matrix[i][j] << "\t";
vector<vector<double> > selectedScores(usersNum, vector<double>(itemsNum,0));
vector<double> line;
for (unsigned i = 0; i < usersNum; ++i)
{
for (unsigned j = 0; j < itemsNum; ++j)
}
//对一个向量中的元素进行降序排列，返回重排后的元素在原来
//向量中的索引
bool IsBigger(double a, double b)
{
return a >= b;
}
vector<unsigned> DescendVector(vector<double> vec)
{
vector<double> tmpVec = vec;
for (unsigned k = 1; k <= length; ++k)
{
hitRate[k-1].push_back(k);
double Counter = 0;
for (unsigned i = 0; i < usersNum; ++i)
{wenku.baidu.com
for (unsigned j = 0; j < k; ++j)
{
line.push_back(scores[i][j]);
}
sortedIndex.push_back(DescendVector(line));
line.clear();
}
//hitRate的第一列存储推荐列表的长度，第二列存储对应的命中率
vector<vector<double> > hitRate(length);
{
res += A[i] * B[i];
}
return res;
}
//矩阵转置操作
template<typename T>//
vector<vector<T> > Transpose(vector<vector<T> > Matrix)
{
unsigned row = Matrix.size();
unsigned col = Matrix[0].size();
double sum = 0;
for (vector<double>::size_type k = 0; k < row; ++k)
{
sum += transData[i][k] * transData[j][k] / SumVector<double>(data[k]);
}
if (degree)
weights[i][j] = sum / degree;
{
//等于0的地方代表user i 还木有评价过item j,需要预测
if (0 == data[i][j])
scores[i][j] = InnerProduct(weights[j],data[i]);
}
}
return scores;
}
//计算推荐结果的命中率:推荐的items中用户确实喜欢的items数量/推荐的items数量
txtWrite(hitRate, "hitRate.txt");
cout << "命中率结果保存完毕！" << endl;
*/
return 0;
}
下面是用户协同过滤核心代码：
#include<iostream>
#include<fstream>
#include<stdio.h>
#include<map>
template<typename T>
void txtWrite(vector<vector<T> > Matrix, string dest)
{
ofstream output(dest);
vector<vector<T> >::size_type row = Matrix.size();
vector<T>::size_type col = Matrix[0].size();
exit(1);
}
vector<vector<T> > data(row, vector<T>(col,0));
for (int i = 0; i < row; ++i)
{
for (int j = 0; j < col; ++j)
{
//因为这里针对的情况是用户只给出对items的喜欢与不喜欢的情况，而movielens
vector<vector<double> > train = txtRead<double>(FilePath1, row, col);
//vector<vector<double> > test = txtRead<double>(FilePath2, 462, 1591);
cout << "利用二部图网络进行评分预测..." << endl;
unsigned length)
{
auto usersNum = test.size();
auto itemsNum = test[0].size();
vector<vector<unsigned> > sortedIndex;
//因为只是对测试集中的用户和items进行测试，于是选取与测试集大小一样的预测数据
{
private:
int k , m ; //k: the most k persons interested in the brand i ; m : choose the former m brands which user u are most interested in
{
if (tmpVec[i] == vec[j])
idx.push_back(j);
}
}
return idx;
}
//基于概率传播(ProbS)的二部图的推荐函数，data是训练数据
vector<vector<double> > ProbS(vector<vector<double> > data)
{
//从TXT中读入数据到矩阵（二维数组）
template <typename T>
vector<vector<T> > txtRead(string FilePath,int row,int col)
{
ifstream input(FilePath);
if (!input.is_open())
{
cerr << "File is not existing, check the path: \n" << FilePath << endl;
auto row = data.size();
auto col = data[0].size();
vector<vector<double> > transData = Transpose(data);
//第一步利用概率传播机制计算权重矩阵
vector<vector<double> > weights(col, vector<double>(col, 0));
vector<vector<T> > Trans(col,vector<T>(row,0));
for (unsigned i = 0; i < col; ++i)
{
for (unsigned j = 0; j < row; ++j)
{
Trans[i][j] = Matrix[j][i];
}
}
return Trans;
}
}
//第二步利用权重矩阵和训练数据集针对每个用户对每一个item评分
vector<vector<double> > scores(row, vector<double>(col, 0));
for (vector<double>::size_type i = 0; i < row; ++i)
{
for (vector<double>::size_type j = 0; j < col; ++j)
#include<set>
#include<vector>
#include<cstdlib>
#include<cmath>
#include<cstring>
#include<algorithm>
using namespace std;
const int MAX = 1000;
class UserCF
{
unsigned itemIndex = sortedIndex[i][j];
if (test[i][itemIndex])
++Counter;
}
}
hitRate[k-1].push_back(Counter / (k * usersNum));
}
return hitRate;
}
int main()
}
//求一个向量中所有元素的和
template<typename T>
T SumVector(vector<T> vec)
{
T res = 0;
for (vector<T>::size_type i = 0; i < vec.size(); ++i)
res += vec[i];
return res;
//是一个1-5的评分数据，所以把分数达到3的看作是喜欢，标记为1，小于3的视为
// 不喜欢，置为0
input >> data[i][j];
if (data[i][j] >= 3)
data[i][j] = 1;
else
data[i][j] = 0;
}
}
return data;
}
//把矩阵中的数据写入TXT文件
基于二部图的个性化系统推荐：
#include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include <vector>
#include <algorithm>
#include <iomanip>
using namespace std;
}
output << endl;
}
}
// 求两个向量的内积
double InnerProduct(std::vector<double> A, std::vector<double> B)
{
double res = 0;
for(std::vector<double>::size_type i = 0; i < A.size(); ++i)
{
string FilePath1("data.txt");
//string FilePath2("E:\\Matlab code\\recommendation system\\data\\movielens\\test.txt");
int row = 10;
int col = 10;
cout << "数据读取中..." << endl;
//用户确实喜欢的items是由测试集给出,length表示推荐列表最长为多少，这里将测出
//推荐列表长度由1已知增加到length过程中，推荐命中率的变化
vector<vector<double> > ComputeHitRate(vector<vector<double> > scores, vector<vector<double> > test,
vector<vector<double> > predictScores = ProbS(train);
txtWrite(predictScores, "predictScores.txt");
/*
cout << "计算命中率..." << endl;
vector<vector<double> > hitRate = ComputeHitRate(predictScores, test, 1591);
sort(tmpVec.begin(), tmpVec.end(), IsBigger);
vector<unsigned> idx;
for (vector<double>::size_type i = 0; i < tmpVec.size(); ++i)
{
for (vector<double>::size_type j = 0; j < vec.size(); ++j)