Recommender systems can generally be classified into one of three different groups depending on the algorithmic approach they take to make recommendations:

Collaborative Filtering: Collaborative filtering is a recommender system technique that makes recommendations for a target user by using ratings information from other users. The driving principle behind collaborative filtering is that users that have similar ratings for items have similar tastes.

Content-based Filtering: Content-based Filtering is a recommender system technique that uses data about user preferences and attributes of items to model the likelihood a given user will like a specific item. This type of recommender systems tends to look more like the traditional machine learning models used in supervised learning.

Knowledge-Based: Knowledge-based recommender systems are a class of recommender systems used when there is not a lot of data available. Rules are explicitly programmed based on user preferences and domain knowledge. While an important class of recommender systems, we will not be discussing them in detail in this module.

We’re primarily going to focus on collaborative filtering in the course of this module.

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