public static void Experiments(string algo, string candidate_item, string dataset_name, bool b_social_int, bool b_social_unint, bool b_rating_int, bool b_rating_unint, int[] balance, string version)
{
int num_recommend = 50;
for (int fold = 1; fold <= 1; fold++)
{
string data_name = "SRWR_" + dataset_name;
string result_name = data_name + "_" + version;
if (b_social_int == true)
{
if (b_social_unint == false)
{
result_name = result_name + "_social(int)";
}
else
{
result_name = result_name + "_social(int,unint)";
}
}
else
{
if (b_social_unint == false)
{
}
else
{
result_name = result_name + "_social(unint)";
}
}
if (b_rating_int == true)
{
if (b_rating_unint == false)
{
result_name = result_name + "_rating(int)";
}
else
{
result_name = result_name + "_rating(int,unint)";
}
}
else
{
if (b_rating_unint == false)
{
}
else
{
result_name = result_name + "_rating(unint)";
}
}
StreamReader social = new StreamReader(file_path + data_name + "_social_bi.txt");
StreamReader social_unint = new StreamReader(file_path + data_name + "_social_predict_500.txt");
StreamReader training = new StreamReader(file_path + data_name + "_rating_train_bi.txt");
StreamReader training_unint = new StreamReader(file_path + data_name + "_rating_train_predict_500.txt");
StreamWriter result = new StreamWriter(file_path + "Result\\" + result_name + ".results");
StreamReader test = new StreamReader(file_path + data_name + "_rating_test.txt");
StreamWriter time = new StreamWriter(file_path + "Result\\" + result_name + ".time");
if (b_social_int == false)
{
social = null;
}
if (b_social_unint == false)
{
social_unint = null;
}
if (b_rating_int == false)
{
training = null;
}
if (b_rating_unint == false)
{
training_unint = null;
}
Stopwatch sw = new Stopwatch();
sw.Start();
if (algo == "SignedRWR_SoRec")
{
double beta = 0.9d;
double gamma = 0.9d;
double delta = 0.5d;
var recsys = new SignedRWR_Recommender(candidate_item, num_recommend, fold, test, beta, gamma, delta, num_user, balance, social, social_unint, training, training_unint);
recsys.Recommend();
recsys.PrintResults(result);
}
else
{
break;
}
sw.Stop();
time.WriteLine(algo + "\t" + sw.ElapsedMilliseconds.ToString() + "ms");
Console.WriteLine(algo + "\t" + sw.ElapsedMilliseconds.ToString() + "ms");
time.Close();
result.Close();
}
}
public static void Experiments(string algo, string candidate_item)
{
int num_recommend = 50;
for (int fold = 1; fold <= 1; fold++)
{
if (candidate_item == "Longtail_items")
{
tophead_items = new HashSet <int>();
sr = new StreamReader(file_path + "raw\\longtail\\u" + fold + "_longtail_items.txt");
while (!sr.EndOfStream)
{
int item_id = int.Parse(sr.ReadLine().ToString()) + num_user;
tophead_items.Add(item_id);
}
sr.Close();
}
StreamReader training = new StreamReader(file_path + "unint\\basic\\u" + fold + "\\u" + fold + "_balance.base");
StreamReader test = new StreamReader(file_path + "raw\\basic\\u" + fold + "\\u" + fold + ".test");
StreamWriter result = new StreamWriter(file_path + "results\\" + algo + "\\unint\\origin\\u" + fold + "_" + algo + "_rankresult_balance(" + candidate_item + ").results");
StreamWriter time = new StreamWriter(file_path + "results\\" + algo + "\\unint\\origin\\u" + fold + "_" + algo + "_rankresult_balance(" + candidate_item + ").time");
Stopwatch sw = new Stopwatch();
sw.Start();
if (algo == "SeparateRWR")
{
var recsys = new SeparateRWR_Recommender(candidate_item, num_recommend, fold, training, test);
recsys.Recommend();
recsys.PrintResults(result);
}
else if (algo == "SignedRWR")
{
double beta = 0.9d;
double gamma = 0.9d;
var recsys = new SignedRWR_Recommender(candidate_item, num_recommend, fold, training, test, beta, gamma);
recsys.Recommend();
recsys.PrintResults(result);
}
else if (algo == "SeparateBP")
{
int num_iter = 5;
double propagation_alpha = 0.0001d;
var recsys = new SeparateBP_Recommender(candidate_item, num_iter, num_recommend, propagation_alpha, fold, training, test);
recsys.Recommend();
recsys.PrintResults(result);
}
else if (algo == "SignedBP")
{
int num_iter = 5;
double propagation_alpha = 0.0001d;
var recsys = new SignedBP_Recommender(candidate_item, num_iter, num_recommend, propagation_alpha, fold, training, test);
recsys.Recommend();
recsys.PrintResults(result);
}
else if (algo == "RWR")
{
var recsys = new RWR_Recommender(candidate_item, num_recommend, fold, training, test);
recsys.Recommend();
recsys.PrintResults(result);
}
sw.Stop();
time.WriteLine(algo + "\t" + sw.ElapsedMilliseconds.ToString() + "ms");
Console.WriteLine(algo + "\t" + sw.ElapsedMilliseconds.ToString() + "ms");
time.Close();
result.Close();
}
}
