public string ListUnionGet()
{
BLL.Recommend_ResouceBLL bll = new BLL.Recommend_ResouceBLL();
string ii = Newtonsoft.Json.JsonConvert.SerializeObject(bll.ListUnion("sa16045"));
Dictionary <string, string> dic = new Dictionary <string, string>();
dic.Add("您可能需要这些资源", ii);
string result = Newtonsoft.Json.JsonConvert.SerializeObject(dic);
return(result);
}
public string GetAll(Models.MyUserModels myUserModels)
{
if (myUserModels == null || myUserModels.UId == null || myUserModels.UId == "")
{
return("error：用户名不对！");
}
else
{
BLL.Recommend_ResouceBLL bll = new BLL.Recommend_ResouceBLL();
string ii = Newtonsoft.Json.JsonConvert.SerializeObject(bll.GetAll(myUserModels.UId));
return(ii);
}
}
public string ListUnion(Models.MyUserModels myUserModels)
{
if (myUserModels == null || myUserModels.UId == null || myUserModels.UId == "")
{
return("error：用户名不对！");
}
else
{
BLL.Recommend_ResouceBLL bll = new BLL.Recommend_ResouceBLL();
string ii = Newtonsoft.Json.JsonConvert.SerializeObject(bll.ListUnion(myUserModels.UId));
Dictionary <string, string> dic = new Dictionary <string, string>();
dic.Add("您可能需要这些资源", ii);
string result = Newtonsoft.Json.JsonConvert.SerializeObject(dic);
return(result);
}
}
public int operationDistance()
{
SqlHelper sh = new SqlHelper();
sh.Open();
OperationDAL opt = new OperationDAL(sh);
opt.DeleteRecommend(); //删除推荐结果表中数据
try {
//1、取出所有老师的ID
List <string> techIDs = opt.getAllTechID();
//2、构造每个老师的评分表
Dictionary <string, Dictionary <string, double> > dic = new Dictionary <string, Dictionary <string, double> >();
dic = opt.makeMarkTable(techIDs);
//3、计算距离（数据稀疏-->余弦相似度；分数膨胀-->皮尔逊；稀疏且膨胀-->修正余弦相似度）
//(1).计算每个老师对资源打分的平均值 这部分存在问题
//Dictionary<string, double> dicAveg = new Dictionary<string, double>();
//foreach (string tID in dic.Keys)
//{
// double sum = 0.0;
// //遍历教师tID对资源的评分
// foreach (string res in dic[tID].Keys)
// {
// sum = sum + dic[tID][res];
// }
// double aveg = sum / dic[tID].Count;
// dicAveg.Add(tID, aveg);
//}
//计算每个用户同其他用户的（曼哈顿）距离，并按远近排序
Dictionary <string, Dictionary <string, double> > sortDic = new Dictionary <string, Dictionary <string, double> >();
foreach (string tid in dic.Keys)
{
Dictionary <string, double> neghborDic = nearestNeighbor(tid, dic);
sortDic.Add(tid, neghborDic);
}
//4.在排序后的评分字典中为每个取出最相近的K个，把这些用户评分高并且推荐目标没有用过的资源作为推荐项，把推荐结果保存（更新）到数据库
foreach (string tid in sortDic.Keys)
{
Dictionary <string, double> dicJuLi = sortDic[tid];
List <string> techList = new List <string>();
int K = 5; //K近邻
List <string> recomlist = new List <string>();
for (int i = 0; i < K; i++)
{
KeyValuePair <string, double> kvp = dicJuLi.ElementAt(i);
foreach (string res in dic[kvp.Key].Keys)
{
if (dic[kvp.Key][res] > 2 && !recomlist.Contains(res))
{ //评分大于3的加入推荐候选项列表
recomlist.Add(res);
}
}
}
//找出本用户已经用过的资源列表
List <string> usedlist = new List <string>();
foreach (string res in dic[tid].Keys)
{
usedlist.Add(res);
}
//recomlist和usedlist做差集
recomlist = recomlist.Except(usedlist).ToList <string>();
if (recomlist.Count < 5)
{ //推荐项少于5，补充社会化属性标签推荐结果
BLL.Recommend_ResouceBLL bll = new BLL.Recommend_ResouceBLL();
List <string> recomlistB = bll.ListUnion(tid);
foreach (string res in recomlistB)
{
if (!recomlist.Contains(res) && recomlist.Count < 5)
{
recomlist.Add(res);
}
}
}
//把用户ID和推荐结果集作为参数调用插入数据库的方法
int temp = opt.saveResult(tid, recomlist);
}
}
catch (Exception ex)
{
return(0);
}
return(1);
}