diff --git a/README.md b/README.md
index 9f25bfd..b7a355e 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,16 @@ For clarify, we use MovieLens-100k dataset as a example and treat the BPRMF meth
    ```python
    python run_pair_mf_train.py --dataset=ml-100k --prepro=origin
    ```
+   And for different teacher models, we could run the code as follows:
+   
+   |  Teacher Model  | Corresponding run file      |
+   |  :----:  | :----:|
+   | MostPop  | `run_mostpop_train.py` |
+   | ItemKNN  | `run_itemknncf_train.py` |
+   | BPRMF    |`run_pair_mf_train.py`|
+   | Item2Vec |`run_item2Vec_train.py`|
+   | NeuMF    |`run_pair_neumf_train.py`|
+   You can also use `--help` command to get more arguments information.
 4. Finally, we train the student model and generate the refined recommendation lists on test set. More details of arguments are available in help message : `python run_trd.py --help`
    ```python
    python run_trd.py --dataset=ml-100k --prepro=origin --method=bprmf --n_actions=20 --pred_score=0
diff --git a/experiment/compute_first_position.py b/experiment/compute_first_position.py
deleted file mode 100644
index 8875fe8..0000000
--- a/experiment/compute_first_position.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import pandas as pd
-import numpy as np
-import csv
-
-import random
-import math
-import gc
-import argparse
-
-from collections import defaultdict
-
-import sys
-# sys.path.append('/home/xinghua/Hongyang/Code-submit')
-sys.path.append('/home/workshop/lhy/code-submit')
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='compute first position')
-    parser.add_argument('--dataset', 
-                        type=str, 
-                        default='ml-100k', 
-                        help='select dataset')
-    parser.add_argument('--method', 
-                        type=str, 
-                        default='bprmf', 
-                        help='mostpop, itemknn, bprmf, item2Vec, neumf')
-    parser.add_argument('--prepro', 
-                        type=str, 
-                        default='origin', 
-                        help='dataset preprocess op.: origin/5core/10core')
-    parser.add_argument('--test_method', 
-                        type=str, 
-                        default='tfo', 
-                        help='method for split test,options: loo/fo/tfo/tloo')
-    args = parser.parse_args()
-    
-    # for method in ['mostpop', 'itemknn', 'bprmf', 'Item2Vec', 'neumf']:
-    for method in ['ddpg']:
-        df = pd.read_csv(f"./res/{args.dataset}/{method}/{args.dataset}_{args.prepro}_{args.test_method}_{method}.csv")
-        df = df.drop(['0'], axis=1)
-        data = np.array(df)
-        data_list = data.tolist()
-        total_first_position = []
-    
-        for p in data_list:
-            position = []
-            if 1 in p:
-                first_position = p.index(1) + 1
-                if first_position > 50:
-                    first_position = 100
-            else:
-                first_position = 100
-            total_first_position.append(first_position)
-        average_first_position = np.mean(total_first_position)
-        print(method, ":the average first position is", average_first_position)
-    
-        """compute confidence interval"""
-        total_num = len(total_first_position)
-        boot_means = []
-        sample_num = int(total_num * 0.95)
-        down = 5
-        up = 100 - down
-        for _ in range(10000):
-            boost_sample = random.sample(total_first_position, sample_num)
-            sample_mean = np.array(boost_sample).mean()
-            boot_means.append(sample_mean)
-        print(method, np.percentile(boot_means, down), np.percentile(boot_means, up))
-        # ground_position = 0
-        # for i in range(0, len(p)):
-        #     if p[i] == 1:
-        #         ground_position += i
-        # p.reverse()
-        # if 1 in p:
-        #     end_position = len(p) - p.index(1)
-        #     count = pd.value_counts(p)[1]
-        #     avg_position =  (ground_position + 1 )/ count
-        # else:
-        #     end_position = 1000
-        #     count = 0
-        #     avg_position = 1000
-
-
-    
\ No newline at end of file