At Schibsted, we use data science to build models that aggregate user behaviors and preferences. Advertisers can combine these to allocate users in groups, known as targeting segments.
In the era of BigData, where the volume of information we manage is so huge that it doesn’t fit into a relational database, many solutions have appeared. Hadoop, Spark, NoSQL are great tools for a purpose, but they don’t fit 100% of the audience.
JSLT is a language for querying and transforming JSON that runs 9 billion transforms every day at Schibsted. We are now open sourcing it so anyone can use it.
Test Impact Analysis (TIA) is a modern way of speeding up the test automation phase of a build. It works by analyzing the minimum set of tests that need to be run after a change to production code.
Kotlin is the new chaos that suddenly penetrated all the tech teams all over the world. Google fully supported Kotlin, integrated it into their Android Studio IDE and considered it a stable language. As a result of this movement, soft or even brute actions need to be considered to be
Imagine this: You’re a marketplace website. You rely on content created by users to deliver a quality product to the general public, with minimum intervention from your side. Because at the end, you’re just a connection between the buyer and the seller. Now, how do you deliver the best user
In an earlier post we discussed a service to estimate the number of unique visitors to a website. In this article we explore the algorithms we used to build the system: HLL (HyperLogLog) and KMV (Kth Minimal Value) and evaluate each.
Cross departmental collaboration within media companies is essential to identity the user, editorial and business requirements when developing products. Identifying and utilising key stakeholder domain expertise is fundamental to delivering on the company and product objectives. Startups are naturally organised for cross functional collaboration, the size of a startup makes
So you have been working on your e-commerce platform and you are almost ready for joining the Hotsale Season (BuenFin or Black Friday). Below you can find a preparation guide and a checklist to make sure your platform will not let you down.
How we used Machine Learning to increase telemarketing conversion rates by 540%.
At leboncoin, we’re migrating to a microservices architecture. Here’s why we’re doing it, and what we’ve learned.
Developing UI at scale can be challenging. At Schibsted, our large, distributed team of iOS developers collaborates on a single codebase. Our modular UI is shared by several apps, each of which applies a unique theme. Our requirements are effectively the worst case scenario for using Storyboards and Interface Builder.