public simi[,] OrderedSimilarityMatrix = new simi[itemTotal, itemTotal]; //排序后的相似性矩阵
/// <summary>
/// 将数据集分成训练集 和 测试集 ----------------Success
/// </summary>
/// <param name="i"></param>
/// <param name="j"></param>
public void SplitDataSet(int i, int j)
{
Array.Clear(TestSet, 0, 3952 * 6040);
Array.Clear(TrainSet, 0, 3952 * 6040);
Thread.Sleep(1000);
string sqlStr = "select * from Ratings";
var dataReader = SqlSeverProvider.ExecuteReader(sqlStr);
while (dataReader.Read())
{
var userID = dataReader.GetInt32(0);
var itemID = dataReader.GetInt32(1);
var rate = dataReader.GetInt32(2);
Random rand = new Random(DateTime.Now.Millisecond);
try
{
if (rand.Next() % (i - 1) == j)
{
TestSet[itemID - 1, userID] = 1;
}
else
{
TrainSet[itemID - 1, userID - 1] = 1;
}
}
catch { }
}
}
/// <summary>
/// 计算所有用户的兴趣程度矩阵
/// </summary>
public void GetUserInterestMatrix()
{
Array.Clear(UserInterestMatrix, 0, 3952 * 6040);
for (int i = 0; i < userTotal; i++)
{
DataTable Tb_UserInterest = new DataTable();
Tb_UserInterest.Columns.Add("userID", System.Type.GetType("System.Int32"));
Tb_UserInterest.Columns.Add("itemID", System.Type.GetType("System.Int32"));
Tb_UserInterest.Columns.Add("interest", System.Type.GetType("System.Double"));
string tableName = "UserInterest";
for (int j = 0; j < itemTotal; j++)
{
if (TrainSet[j, i] == 0)
{
UserInterestMatrix[i, j] = CalculateUserInterest(i, j, 10);//----------K默认是10
DataRow row = Tb_UserInterest.NewRow();
row[0] = i + 1;
row[1] = j + 1;
row[2] = UserInterestMatrix[i, j];
Tb_UserInterest.Rows.Add(row);
}
}
SqlSeverProvider.ExecuteSqlBulkCopy(Tb_UserInterest, tableName);
}
Console.WriteLine("Tb_UserInterest OK ");
}
public static void Insert(int userId, int itemId, int rate)
{
string sqlStr = "insert into tb_TestRatings values (@userID,@itemID,@rating)";
SqlParameter[] parms =
{
new SqlParameter("@userID", userId),
new SqlParameter("@itemID", itemId),
new SqlParameter("@rating", rate)
};
var result = SqlSeverProvider.ExecuteNonQuery(sqlStr, parms);
}
/// <summary>
/// 推荐物品
/// </summary>
public void GetRecommend()
{
int mostInterestNum; //当前最感兴趣物品
for (int i = 0; i < userTotal; i++)
{
int [] orderFlag = new int[itemTotal];
for (int j = 0; j < recommendNum; j++)
{
mostInterestNum = 0;
while (orderFlag[mostInterestNum] != 0)
{
mostInterestNum++;
}
for (int k = 0; k < itemTotal; k++)
{
if (UserInterestMatrix[i, mostInterestNum] < UserInterestMatrix[j, k] && orderFlag[k] == 0)
{
mostInterestNum = k;
}
}
orderFlag[mostInterestNum] = 1;
RecommendSet[i, j] = mostInterestNum;
}
}
Console.WriteLine("推荐完成...");
for (int i = 0; i < 6040; i++)
{
DataTable Tb_Recommended = new DataTable();
Tb_Recommended.Columns.Add("userID", System.Type.GetType("System.Int32"));
Tb_Recommended.Columns.Add("itemID", System.Type.GetType("System.Int32"));
string tableName = "Recommended";
for (int j = 0; j < 10; j++)
{
var item = (int)RecommendSet[i, j];
//string str = string.Format("insert into Recommended values({0},{1})",i+1,item);
//SqlSeverProvider.ExecuteNonQuery(str);
DataRow row = Tb_Recommended.NewRow();
row[0] = i + 1;
row[1] = item;
Tb_Recommended.Rows.Add(row);
}
SqlSeverProvider.ExecuteSqlBulkCopy(Tb_Recommended, tableName);
}
Console.WriteLine("推荐完成22222...");
}
/// <summary>
/// 计算所有物品之间的相似性，得到相似性矩阵 --------------Success
/// </summary>
///
//DataTable Tb_SimilarityMatrix = new DataTable();
//table.Columns.Add("userID", System.Type.GetType("System.Int32"));
//table.Columns.Add("itemID", System.Type.GetType("System.Int32"));
//table.Columns.Add("rating", System.Type.GetType("System.Int32"));
//string tableName = "tb_TestRatings";
// //sqlbulkCopy
// //DataRow row = table.NewRow();
// //row[0] = userId;
// //row[1] = itemId;
// //row[2] = rating;
// //table.Rows.Add(row);
//// SqlSeverProvider.ExecuteSqlBulkCopy(table, tableName);
public void GetSimilarityMatrix()
{
Array.Clear(SimilarityMatrix, 0, 3952 * 3952);
for (int i = 0; i < itemTotal; i++)
{
DataTable Tb_SimilarityMatrix = new DataTable();
Tb_SimilarityMatrix.Columns.Add("itemOne", System.Type.GetType("System.Int32"));
Tb_SimilarityMatrix.Columns.Add("itemTwo", System.Type.GetType("System.Int32"));
Tb_SimilarityMatrix.Columns.Add("similarity", System.Type.GetType("System.Double"));
string tableName = "SimilarityMatrix";
for (int j = 0; j < itemTotal; j++)
{
SimilarityMatrix[i, j] = CalculateSimilarityForA_B(i, j);
// double similar = CalculateSimilarityForA_B(i, j);
DataRow row = Tb_SimilarityMatrix.NewRow();
row[0] = i + 1;
row[1] = j + 1;
row[2] = SimilarityMatrix[i, j];
Tb_SimilarityMatrix.Rows.Add(row);
//double similar= CalculateSimilarityForA_B(i, j);
//string sqlStr = string.Format("insert into SimilarityMatrix values({0},{1},{2})",i+1,j+1,similar);
//SqlSeverProvider.ExecuteNonQuery(sqlStr);
}
SqlSeverProvider.ExecuteSqlBulkCopy(Tb_SimilarityMatrix, tableName);
}
Console.WriteLine("Tb_SimilarityMatrix OK ");
// 输出相似性矩阵
//for (int i = 0; i < 3; i++)
//{
// Console.WriteLine("item " + i + ":");
// for (int j = 0; j < 100; j++)
// {
// Console.Write(SimilarityMatrix[i, j] + " ");
// }
//}
}
//public int[] GetRecomend(int userId,List<int> ?itemIds)
//{
// List<int> items = new List<int>();
// items.Sort();
// return null;
//}
public int[] GetRecommend(int userId, params int[] itemIds)
{
Dictionary <int, double> recommendedList = new Dictionary <int, double>();
//根据用户id 获取最佳推荐的10个，添加到recommendedList
string sqlStr = string.Format("select top(10) itemID from UserInterest where userID={0} and interest !=0 order by interest desc", userId);
SqlSeverProvider.ExecuteReader(sqlStr);
//根据itemid 获取相似度较高的物品，添加到recommendedList
// select top(20)* from SimilarityMatrix where itemOne=176 order by similarity desc
// select top(10) itemID,COUNT(itemID) as total from Ratings group by itemID order by total desc 高访问率
//根据相似度高低对 recommendedList进行排序
var result = from it in recommendedList orderby it.Value select it;
foreach (var item in result.Take(10))
{
Console.WriteLine(item.Key);
}
return(null);
}
