User profile based recommendations

Now that you have built the user profile based on the aggregate of the individual movies they enjoyed, you can compare it to the larger tfidf_summary_df DataFrame that you have been working with to generate suggestions. As you would not want to suggest movies that the user has already watched, you will first find a subset of the tfidf_summary_df DataFrame that does not contain any of the previously watched movies.

The DataFrame user_prof that you generated in the last exercise that contains a single column representing the user has been loaded for you. Similarly, the list_of_movies_enjoyed has been loaded so you can exclude them from the predictions.

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Building Recommendation Engines in Python

ดูคอร์ส

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

from sklearn.metrics.pairwise import cosine_similarity

# Find subset of tfidf_df that does not include movies in list_of_movies_enjoyed
tfidf_subset_df = tfidf_df.____(list_of_movies_enjoyed, axis=____)

แก้ไขและรันโค้ด