Serving the most relevant information for LinkedIn members in the homepage feed requires a massive effort—hundreds of features are used to personalize content for hundreds of millions of members. For each homepage visit, our machine learning models have to find and surface the best activity across a member's whole network, and they have to source that content...
A/B Testing Articles
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Co-authors: Guillaume Saint-Jacques, Amir Sepehri, Nicole Li, and Igor Perisic Introduction Previously on this blog, we’ve shared information on best practices in data science, particularly in areas such as A/B testing. We’ve also discussed the importance of ethics in fields such as data science, early implementations of “fairness by design” principles in our...
- Topics:
- A/B Testing,
- data science,
- experimentation,
- research,
- Product Design,
- Data,
- machine learning,
- T-REX
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Co-authors: Alexander Ivaniuk, Jingbang Liu At LinkedIn, we like to say that experimentation is in our blood because no production release at the company happens without experimentation; by “experimentation,” we typically mean “A/B testing.” The company relies on employees to make decisions by analyzing data. Experimentation is a data-driven foundation of the...
- Topics:
- Developer Productivity,
- A/B Testing,
- experimentation,
- T-REX,
- Data
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“Make LinkedIn feel instantaneous” is a mantra our performance team lives by to deliver the best possible experience to our members....
- Topics:
- Performance,
- A/B Testing,
- experimentation,
- T-REX
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At LinkedIn, most decisions are made using experiments. When we want to decide between two features, we test them against each other...
- Topics:
- A/B Testing,
- Testing,
- data science,
- experimentation,
- T-REX,
- Data
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At LinkedIn, we use data to improve our members’ experiences while using our site. On the video team, we value metrics that yield...
- Topics:
- A/B Testing,
- experimentation,
- NYC Engineering,
- Data,
- Performance,
- Video,
- T-REX