Python for Machine Learning - The Complete Beginners Course - Content-Based Recommender System

The video tutorial explains content-based filtering, a method that uses product features to recommend items similar to what a user likes. It highlights how user preferences, previous actions, and explicit feedback like ratings influence recommendations. An example is provided where a user who likes adventurous movies is recommended another adventurous movie, demonstrating the system's logic.

What is the primary method used by content-based filtering to recommend products?

• Social media activity

• Product features

• Purchase history

• User demographics

Which of the following is NOT a factor considered in content-based filtering?

• Product features

• Friends' recommendations

• User's previous actions

• Explicit feedback

What is the role of explicit feedback in content-based filtering?

• To track the user's browsing history

• To adjust recommendations based on user ratings

• To determine the user's location

• To connect with social media accounts

In the given example, what genre of movies does user one prefer?

• Horror

• Adventurous

• Drama

• Comedy

How does content-based filtering determine the next movie suggestion for user one?

• By asking friends for recommendations

• By considering the latest releases

• By analyzing the genre of previously liked movies

• By checking the most popular movies