A content-based recommendation system uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.
Step 1: Import and visualize the dataset and select the features to filter recommended items.
Step 2: Combine all the selected features in a single column.
Step 3: Tokenize the combined column using a count vectorizer and convert the count matrix into a cosine matrix.
Step 4: Recommend items based on cosine similarity.
Step 5: Change the selected features to get varying results.