Apache Spark For Movie Recommendation
Netflix uses Apache Spark and Machine learning for Movie recommendations. Let’s understand how it works with an example.
When you load the front page you see multiple rows of different kinds of movies. Netflix personalizes this data and decides what kind of rows or what kind of movies should be displayed to a specific user. This data is based on the user’s historical data and preferences.
Also, for that specific user, Netflix performs sorting of the movies and calculates the relevance ranking (for the recommendation) of these movies available on their platform. In Netflix, Apache Spark is used for content recommendations and personalization.
A majority of the machine learning pipelines are run on these large spark clusters. These pipelines are then used to do row selection, sorting, title relevance ranking, and artwork personalization among others.
Video Recommendation System
If a user wants to discover some content or video on Netflix, the recommendation system of Netflix helps users to find their favorite movies or videos. To build this recommendation system Netflix has to predict the user interest and it gathers different kinds of data from the users such as:
- User interaction with the service (viewing history and how the user rated other titles)
- Other members with similar tastes and preferences.
- Metadata information from the previously watched videos for a user such as titles, genre, categories, actors, release year, etc.
- The device of the user, at what time a user is more active, and for how long a user is active.
- Netflix uses two different algorithms to build a recommendation system…
- Collaborative filtering:
- The idea of this filtering is that if two users have similar rating histories then they will behave similarly in the future.
- For example, consider there are two-person. One person liked the movie and rated the movie with a good score.
- Now, there is a good chance that the other person will also have a similar pattern and he/she will do the same thing that the first person has done.
- Content-based filtering:
- The idea is to filter those videos which are similar to the video a user has liked before.
- Content-based filtering is highly dependent on the information from the products such as movie title, release year, actors, the genre.
- So to implement this filtering it’s important to know the information describing each item and some sort of user profile describing what the user likes is also desirable.
System Design Netflix | A Complete Architecture
Designing Netflix is a quite common question of system design rounds in interviews. In the world of streaming services, Netflix stands as a monopoly, captivating millions of viewers worldwide with its vast library of content delivered seamlessly to screens of all sizes. Behind this seemingly effortless experience lies a nicely crafted system design. In this article, we will study Netflix’s system design.
Important Topics for the Netflix System Design
- Requirements of Netflix System Design
- High-Level Design of Netflix System Design
- Microservices Architecture of Netflix
- Low Level Design of Netflix System Design
- How Does Netflix Onboard a Movie/Video?
- How Netflix balance the high traffic load
- EV Cache
- Data Processing in Netflix Using Kafka And Apache Chukwa
- Elastic Search
- Apache Spark For Movie Recommendation
- Database Design of Netflix System Design
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