User-Based Vs Item-Based Recommenders

The video tutorial explains collaborative filtering, focusing on user-based and item-based approaches. It begins with user-based filtering, detailing how to calculate similarity between users and predict ratings. The item-based approach is then discussed, highlighting the calculation of similarity between items. The video concludes with a comparison, noting that item-based filtering is often more effective due to the static nature of items compared to dynamic user preferences. Practical insights are provided, emphasizing the challenges of user-based filtering in large platforms like Netflix.

What is the first step in user-based collaborative filtering?

How do we calculate similarity between users?

Explain the role of the nearest neighbor algorithm in collaborative filtering.

What is the main advantage of item-based collaborative filtering over user-based?

Why might user preferences change over time, and how does this affect collaborative filtering?