RecSys Types in AI & Artificial Intelligence

Content-Based Filtering is the 'Tell me more about this' approach. It builds a profile of the user's preferences based on the Features (metadata) of the items they've interacted with. If you watch three 'Sci-Fi' movies directed by 'Christopher Nolan', the system identifies those two features as high weights in your profile and suggests other Sci-Fi movies or Nolan films. The great advantage here is that it doesn't need data from other users to work—it only needs to know *what* you liked and *what* else is in the catalog.

Collaborative Filtering is the 'People like you' approach. It ignores the features of the items and focuses entirely on the Interaction Matrix. It identifies 'Neighbors' (users who have rated items similarly to you) and recommends items that those neighbors enjoyed but you haven't seen yet. This is powerful because it can suggest items outside of your usual genres—discovering 'Unexpected' gems based on the shared taste of the community.

Most modern companies (Netflix, YouTube) use Hybrid Systems. Content-based systems struggle with 'Serendipity' (they only suggest things similar to what you already know). Collaborative systems struggle with the Cold Start Problem (they can't recommend a brand new item that no one has rated yet). By combining them—often using a content-based model to handle new items and a collaborative model to handle user trends—you build a system that is robust, surprising, and always relevant.

Not all recommendations are created equal. Depending on the data you have, you might use Content-Based, Collaborative, or Hybrid systems to reach your goal.

Content-Based Filtering recommends items similar to what a user liked in the past. It's about 'Items' and their features.

Collaborative Filtering recommends items based on the behavior of *other users*. It's about 'People' and their shared patterns.

Checkpoint: Which system would you use if you have NO data about other users, but you KNOW the features of the items?

• →Collaborative Filtering
• →Content-Based Filtering

Hybrid Systems combine both approaches to eliminate the weaknesses of each, such as the 'Cold Start' problem for new items.

By understanding these types, you can choose the architecture that fits your data availability and your product's specific needs.

Checkpoint: What is a 'Hybrid' system?

• →A manual list of items
• →A system that combines multiple recommendation strategies (like content-based and collaborative) to get better results

Types mastered! You've learned the taxonomy. Ready to dive into the math of Content-Based systems with TF-IDF?

Classify a Real System Type. Finish the rule that classifies a recommender system by which kind of data it relies on.