Netflix uses a hybrid recommender system that combines:
Collaborative Filtering (CF): Learns from user behavior and preferences. If users A and B both liked similar shows, CF assumes they will enjoy other similar content.
Content-Based Filtering: Uses metadata like genre, director, cast, and keywords to recommend items with similar attributes to content you’ve watched.
Contextual Bandits and Deep Learning: Netflix also adapts to time of day, device, or recent watch history using advanced machine learning models.
Yes, but not perfectly. Netflix is generally successful at recommending relevant content. However, there are some issues:
Pros:
Accurate suggestions based on past behavior.
Personalized landing pages and rows.
Variety of content is well-organized into categories.
Cons:
Tends to reinforce viewing habits (filter bubbles).
Hard to discover new content unless it’s trending or featured.
Cold-start problem for brand-new users.
Rotten Tomatoes is a non-personalized recommender system based on aggregated critic and audience reviews.
Tomatometer Score: Percentage of approved critics who gave the film a positive review.
Audience Score: Based on verified audience ratings.
Fresh/Rotten Labels: Simple binary system to classify reviews.
I like Rotten Tomatoes for getting a general sense of how well a movie is received. However, because it’s not personalized:
I use it for initial screening, not final decision-making.
I find the audience score more aligned with my taste than critic reviews.
In the article “Wisdom of the crowd? IMDb users gang up on Christian Bale’s new movie before it even opens” (Washington Post, 2017):
A coordinated effort by online users bombarded the IMDb page of The Promise with negative reviews before its release.
This resulted in an artificially low rating, influencing public perception unfairly.
A similar event occurred with the game The Last of Us Part II, where:
Users unhappy with certain plot points or political themes organized campaigns to leave negative reviews.
Ratings plummeted on platforms like Metacritic within hours of release.
To prevent rating manipulation:
Verification System: Only allow users who’ve purchased or streamed the content to leave a review.
Time-Based Filters: Prevent ratings until a product is officially released.
Reputation Scores: Weight reviews based on user history and trustworthiness.
Anomaly Detection: Use ML models to flag sudden influxes of extreme reviews.
Sentiment Analysis: Compare written reviews to star ratings for consistency.
Recommender systems are powerful but vulnerable to manipulation. As seen in both IMDb and Metacritic, platform integrity depends on moderation and smart algorithm design. Personalization can improve user experience, but ethical and secure design is just as important.