Update app.py

app.py

@@ -157,9 +157,6 @@ autoencoder.fit(movie_user_matrix_scaled, movie_user_matrix_scaled,
                 epochs=50, batch_size=64, shuffle=True, validation_split=0.2, 
                 verbose=0)
 
-
-
-
 # Use the trained autoencoder to predict the complete matrix
 predicted_matrix_scaled = autoencoder.predict(movie_user_matrix_scaled)
 predicted_matrix = scaler.inverse_transform(predicted_matrix_scaled)
@@ -225,30 +222,42 @@ total_movies = len(movies)
 
 with gr.Blocks() as iface:
     with gr.Tab("Content-Based Filtering"):
+        gr.Markdown("""## Movie Recommender - Content-Based Filtering
+        * 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).
+        """)        
         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.)"), 
                      outputs=[gr.Textbox(label="Recommended Movies:")],
-                     title="Movie Recommender - Content-Based Filtering", 
+                     # title="Movie Recommender - Content-Based Filtering", 
                      description="Select a movie to get recommendations based on content filtering.")
     
     with gr.Tab("Collaborative Filtering"):
-        gr.Markdown("""# Movie Recommender - Item-Based Collaborative Filtering
+        gr.Markdown("""## Movie Recommender - Item-Based Collaborative Filtering
         * 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.
+        * Simple to implement and computationally efficient, but doesn't handle sparsity well (when many missing ratings).
         """)
         gr.Interface(fn=recommend_movies_cf,
                      inputs=gr.Dropdown(movie_list, label=f"Select a Movie (Total movies: {total_movies}, randomly list {input_count} for demo purpose.)"),
                      outputs=[gr.Textbox(label="Recommended Movies:")],
-                     title="Movie Recommender - Item-Based Collaborative Filtering",
+                     # title="Movie Recommender - Item-Based Collaborative Filtering",
                      description="Select a movie to get recommendations based on collaborative filtering.")
 
     with gr.Tab("Collaborative Filtering with Neural Network"):
-        gr.Markdown("A Neural Network is used to predict the missing values in the movie-user matrix, which should improve our collaborative filtering recommendations.")
+        gr.Markdown("""## Movie Recommender - Item-Based Collaborative Filtering with Neural Network
+        * 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.
+        * This approach often provides more accurate recommendations especially with large sparse datasets. But more complex to implement and require more computational resources.
+        """)
         gr.Interface(fn=recommend_movies_cfnn,
                      inputs=gr.Dropdown(movie_list, label=f"Select a Movie (Total movies: {total_movies}, randomly list {input_count} for demo purpose.)"),
                      outputs=[gr.Textbox(label="Recommended Movies:")],
-                     title="Movie Recommender - Item-Based Collaborative Filtering",
+                     # title="Movie Recommender - Item-Based Collaborative Filtering",
                      description="Select a movie to get recommendations based on collaborative filtering.")
 
 # Launch the app