1. Popular-based recommendation: Popular-based recommendation systems are straightforward to implement because they don’t require complex algorithms or user-specific data. They tend to rely on simple stats, such as how often a movie is watched, and end up giving the same recommendations to everyone, pushing what’s popular with the masses.
2. Content-based filtering: This approach focuses on the characteristics of the items themselves. It suggests movies that match the user’s interests by analyzing key features like genres, themes, and styles, offering recommendations tailored to their preferences.
3. Collaborative filtering: This method relies on the collective preferences of users. It can be user-based, where recommendations are made based on the preferences of similar users, or item-based, where recommendations are made based on items that are similar to what the user has liked in the past.

A look at the project ahead

• Understand the basic concepts and types of recommendation systems.
• Implement a simple popularity-based recommendation system.
• Implement a content-based recommendation system.
• Implement an item-based recommendation system.

What you’ll need

• No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
• Basic understanding of Python: Some basic understanding of Python will be beneficial.
• Some understanding of statistical concepts: It’s helpful to have some understanding of statistic concepts, particularly Linear Algebra.
• A current version of a web browser: To run the project, you’ll need a web browser like Chrome, Edge, Firefox, or Safari.