Recommender Systems with Machine Learning - Collaborative Filtering and User-Based Collaborative Filtering

The video tutorial discusses collaborative filtering, a recommendation system technique that uses user interactions and data to predict preferences. It explains the concept of collaborative filtering, focusing on user and item relationships, and introduces user-based collaborative filtering, which predicts user preferences based on other users' ratings. The tutorial also covers the nearest neighbor algorithm and highlights the advantages and disadvantages of user-based collaborative filtering, such as ease of implementation, accuracy, sparsity, scalability, and cold start issues.

What is the primary concept behind collaborative filtering?

• It uses content-based analysis to recommend items.

• It relies on user interactions and data to filter information.

• It depends on the time of day for recommendations.

• It focuses on the geographical location of users.

In user-based collaborative filtering, how is a prediction made for a user?

• By using the user's purchase history.

• By evaluating the user's social media activity.

• By considering the ratings given by similar users.

• By analyzing the user's browsing history.

What is a specific application of user-based collaborative filtering?

• Nearest neighbor algorithm

• Content-based filtering

• Time-based filtering

• Geographical filtering

Which of the following is an advantage of user-based collaborative filtering?

• It is highly dependent on the context.

• It is more accurate than content-based filtering.

• It requires a large amount of data to function.

• It is difficult to implement.

What is a major disadvantage of user-based collaborative filtering?

• It requires constant user feedback.

• It is too simple to be effective.

• It struggles with sparsity due to low rating percentages.

• It only works for new users.

Why is scalability a challenge in user-based collaborative filtering?

• Because it relies on outdated technology.

• Because it needs a large number of servers.

• Because it becomes difficult to compute nearest neighbors as users increase.

• Because it requires real-time data processing.

What is the 'cold start' problem in user-based collaborative filtering?

• New users have no initial data for recommendations.

• It only works in cold climates.

• It takes a long time to start the system.

• The system requires a lot of energy to start.