Ten years ago, we published a post in the Netflix tech blog explaining our three-tier architectural approach to building recommender systems (see below). A lot has happened in the last 10 years in the recommender systems space for sure. That’s why, when a few months back I designed a Recsys course for Sphere, I thought it would be a great opportunity to revisit the blueprint.
In this blog post I summarize 4 existing architectural blueprints, and present a new one that, in my opinion, encompasses all the previous ones.
At a very high-level, any recommender system has items to score and/or rank, and a machine learned model that does that. This model needs to be trained on some data, which is obtained from the service where the recommender operates in some form of feedback loop. The architectural blueprints that we will see below connect those components (and others) in a general way while incorporating some best practices and guidelines.
The Netflix three tier architecture
In our post ten years ago, we focused on clearly distinguishing the components that can be executed offline (i.e. not when the recommendations need to be served but rather e.g. once a day), those that need to be computed online (i.e. when the user visits the site and the recommendation is being served) and those somewhere in the middle called nearline (i.e. components that are executed when the user visits the site, but do not need to be served in real-time). At that time, and still today in many cases, most of the big data training of the algorithm was performed offline using systems such as Hadoop or Spark. The nearline layer included things like filtering in response to user events, but also some retraining capabilities such as e.g. folding-in and incremental matrix factorization training (see here for a practical introduction to the topic).
To continue reading this article, click here.
You must be logged in to post a comment.
 |
 |
 |
dordle game is a Wordle alternative, a clone made for players who feel like they only need to guess 3-4 times to find Wordle every day.
Really enjoyable to read. It was a pleasure to read your useful content. Interesting article. Thanks for this; I’ve been thinking about this. Very a fantastic post. This post is quite great and helpful. basketball stars
It’s interesting to see how technology and the field of recommendation systems have evolved over the past decade. The clear distinction between offline, nearline, and online components that you mentioned is still relevant today. However, the way in which these components are executed and the tools used have changed. Affiliate Marketing SEO
The article highlights the significance of the book “Blueprints for Recommender Systems” in the field of data science and machine learning, particularly for building recommender systems. The book’s practical approach and comprehensive coverage make it a valuable resource for professionals and researchers interested in this area.
headstone engraving
The delicate balance between preserving the Geometry Dash integrity of the text and maintaining its quality and usability becomes a crucial factor in the implementation of effective watermarking techniques.
I like your post about this 10th anniversary edition and in this time you achieved so much. When I hired the professional painters irving tx I got surprised with their service quality and it is of high standards.
Super Mario is a popular and iconic video game franchise created by Nintendo, featuring the adventures of an Italian plumber named Mario and his brother Luigi.
Great design, I will look back and admire it. io games
I enjoyed your article. Thank you for sharing such valuable information with us. I hope you continue to write such great content. Also, Visit here toy poodle price in india