diff --git a/recommender_system/README.en.md b/recommender_system/README.en.md
index 8e3d9a9dc5b1fa05ec9deef6d38ffc42f44f3fa8..637b336b031ea7fb9657d4d08b7eb0187b9af526 100644
--- a/recommender_system/README.en.md
+++ b/recommender_system/README.en.md
@@ -76,8 +76,6 @@ Figure 3. A hybrid recommendation model.
 
 ## Dataset
 
-### Data preparation and downloading
-
 We use the [MovieLens ml-1m](http://files.grouplens.org/datasets/movielens/ml-1m.zip) to train our model.  This dataset includes 10,000 ratings of 4,000 movies from 6,000 users to 4,000 movies.  Each rate is in the range of 1~5.  Thanks to GroupLens Research for collecting, processing and publishing the dataset.
 
 `paddle.v2.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens` and `wmt14`, etc. There's no need for us to manually download and preprocess `MovieLens` dataset.