Feed Recommendation Algorithm Data Sampling

This post details the engineering effort behind refining the LinkedIn member experience by experimenting with data sampling techniques for the feed recommendation algorithm. The work involved handling large-scale datasets (up to 500 million rows) and utilizing big data technologies like Spark and Hadoop to feed these sampled datasets into machine learning algorithms, resulting in business metric gains. The post also touches upon on-call responsibilities for the global feed, including data generation pipelines.