Update app.py

app.py

@@ -223,10 +223,11 @@ total_movies = len(movies)
 with gr.Blocks() as iface:
     with gr.Tab("Content-Based Filtering"):
         gr.Markdown("""## Movie Recommender - Content-Based Filtering
+        How it works:
         * Use the 'genres' feature of movies, and convert genres into numerical vectors.
         * For a given movie, find the most similar movies based on the genre similarity.
         * This approach uses genres of movies only, without considering user preferences or viewing history.
-        * Simple to implement and computationally efficient, but doesn't handle sparsity well (when many missing ratings).
+        * Simple to implement and computationally efficient.
         """)        
         gr.Interface(fn=recommend_movies_cb, 
                      inputs=gr.Dropdown(movie_list, label=f"Select a Movie (Total movies: {total_movies}, randomly list {input_count} for demo purpose.)"), 
@@ -236,6 +237,7 @@ with gr.Blocks() as iface:
     
     with gr.Tab("Collaborative Filtering"):
         gr.Markdown("""## Movie Recommender - Item-Based Collaborative Filtering
+        How it works:
         * Create a movie-user matrix where rows represent movies and columns represent users, each cell contains the rating a user gave to a movie, or 0 if no rating exists.
         * Calculate the cosine similarity between movies based on their rating patterns, results in a movie-movie similarity matrix.
         * For a given movie, find the most similar movies based on this similarity matrix, and recommend these movies.
@@ -249,6 +251,7 @@ with gr.Blocks() as iface:
 
     with gr.Tab("Collaborative Filtering with Neural Network"):
         gr.Markdown("""## Movie Recommender - Item-Based Collaborative Filtering with Neural Network
+        How it works:
         * Use a Neural Network to predict the missing values in the movie-user matrix to improve the collaborative filtering recommendations.
         * The NN model learns to reconstruct the movie-user matrix, effectively predicting missing ratings. This results in a dense, predicted movie-user matrix.
         * Calculate movie-movie similarities using the predicted matrix. And use this similarity matrix to find and recommend similar movies.