Recommender System Discussion Post

I’ve decided to analyze the Netflix Recommender System. Netflix is one of the most widely used streaming platforms globally and is well known for its ability to deliver highly personalized recommendations to its users. According to Netflix, over 80% of what people watch comes from some form of recommendation. Since they have such a huge content catalog, the company depends heavily on its recommender system to help users quickly find something worth watching, without being overwhelming.

Scenario Design Analysis - Netflix

Who are their target users?

• New subscribers who are exploring the platform for the first time
• Returning subscribers who may have canceled and are considering rejoining
• Existing users with high engagement
• Viewers of all demographics across different regions, languages, and cultures
• Niche audiences for specific genres (anime, true crime etc.)

What are their key goals?

• Maximize user engagement and time spent watching
• Keep users satisfied and subscribed
• Recommend content that aligns with individual preferences
• Promote Netflix original content strategically
• Provide a seamless, easy-to-navigate interface

How can they accomplish these goals?

• Offering a personalized homepage experience
• Using machine learning to recommend content that users are most likely to enjoy
• Promoting content through smart placement (such as top picks or trending now)
• Minimizing decision fatigue with autoplay previews and easy-to-browse carousels

Scenario Design Analysis - Customer

Who are their target users?

• New subscribers with little to no watch history
• Long-time users with extensive viewing habits
• Families and shared accounts with varied interests
• Users across different regions and languages

What are their key goals?

• Discover content that they are interested in
• Get recommendations for similar shows/movies they’ve enjoyed
• Quickly find shows or movies to watch without excessive browsing
• Create or manage personalized watchlists

How can they accomplish these goals?

• Options to ‘like’ or ‘dislike’ content
• Watchlists to save content for future viewing
• Creating profiles for different users on the same account
• Autoplaying previews to quickly sample new shows/movies

Reverse Engineering Netflix’s Recommender System

Netflix’s recommendation engine works when users watch their first show or movie. Based on their initial viewing, their homepage starts changing and suggesting similar content or shows enjoyed by others with similar habits. The system uses a mix of collaborative filtering (what similar users liked), content-based filtering (based on metadata like genre and cast) and more advanced deep learning models.

They also considers contextual signals like time of day, device type, and watching patterns (binge sessions compared to casual viewing). For example, a viewer who watches cartoons on Saturday mornings may get family-friendly recommendations then, while thriller recommendations might show up at night.

Netflix’s homepage essentially introduces its recommender system. Sections like “Top Picks for You,” “Continue Watching,” “Trending Now,” and “Because You Watched” all stem from different models working together. Their recommendation system is deeply integrated into every part of the platform. It was so interesting to learn that even the thumbnails users see for the same title might vary based on what type of imagery has historically led to clicks from them. The system is always learning and using real-time behavior (like what you hover over, how long you watch, when you stop etc) to improve future recommendations.

Recommendations to improve the Recommender System

• Add more detailed feedback options
  • Currently, Netflix only offers thumbs up/down (or the double thumbs up). Allowing users to give more specific feedback, such as “liked the actor” or “enjoyed the humor,” could help to refine the system and provide a clearer signal on what exactly the viewer enjoyed the most.
• Incorporate Social Features (CAREFULLY)
  • Adding a social integration feature to see what friends are watching (with privacy controls, of course) or allowing users to share and recommend content with others could influence more exploration.
• Add Viewing Modes
  • Some viewers use Netflix differently depending on the moment, their mood or time of day. The creation of features like a “relax” mode where they might suggest light-hearted comedies, or a “focus” mode where they might suggest documentaries can help to enhance engagement based on users’ mood or the context.