update readme

README.md

@@ -6,14 +6,19 @@
   <img src='https://img.shields.io/badge/scikit_learn-0.23.2+-brightgreen'>
   <img src='https://img.shields.io/badge/pandas-1.0.5+-brightgreen'>
   <img src="https://img.shields.io/pypi/l/torch-rechub">
+  <img src='https://img.shields.io/badge/annoy-1.17.0-brightgreen'>
   <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fmorningsky%2FTorch-RecHub%2F&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com">
 
+
 A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
 
 ## 安装
 
 ```python
+#稳定版
 pip install torch-rechub
+#最新版
+git clone https://github.com/morningsky/Torch-RecHub.git
 ```
 
 ## 主要特性

examples/run_movielens.py

@@ -111,7 +111,7 @@ def main(dataset_path, model_name, epoch, learning_rate, batch_size, weight_deca
     print("matching for topk")
     user_map, item_map = np.load("./data/ml-1m/saved/raw_id_maps.npy", allow_pickle=True)
     match_res = collections.defaultdict(dict)
-    topk = 100
+    topk = 10
     for user_id, user_emb in zip(test_user_model_input["user_id"], user_embedding):
         items_idx, items_scores = annoy.query(v=user_emb, n=topk) #the index of topk match items
         match_res[user_map[user_id]] = all_item_model_input["movie_id"][items_idx]