‘Rex’: Medium’s Recommender-System as a Microservice [Miles Hinson]
Miles Hinson writes about his experience in developing ‘Rex’, the recommender system for Medium, as a Microservice. He discusses design choices and presents a few numbers such as that 95% of all recommendations are created within less than 1 second.
However, the biggest issue was language choice. Much of Medium runs within a Node.js monolith, including the code that used to power story recommendations. Node, despite its many strengths, it wasn’t the best tool for this particular task.
https://medium.engineering/rex-mediums-go-recommendation-microservice-e077bc9582a
How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages? [Dokyun Lee & Kartik Hosanagar]
Case Study on how VistaPrint uses Amazon Personalize for product recommendations
Against All Odds: Netflix, From Mail-Order DVD Rentals to Streaming Dominance [Shawn Knight @ TechSpot]
About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).