Sam Shah and Pete Skomoroch
Endorsements are a one-click system to recognize someone for their skills and expertise on LinkedIn, the largest professional online social network. This is one of the latest “data features” in LinkedIn’s portfolio, and the endorsement ecosystem generates a large graph of reputation signals and viral user activity.
Underneath this feature, there are several interesting and difficult data questions:
- How do you automatically create a taxonomy of skills in the professional context?
- How do you disambiguate between different contexts of skills? For instance, “search” could mean information retrieval, search & seizure, search & rescue, among others.
- How can you leverage data to determine someone’s authoritativeness in a skill?
- How do you use that authoritativeness to recommend people to endorse?
- How do you optimize a complex large scale machine learning system for viral growth & engagement?
In this talk, we’ll examine the practical aspects of building a data feature like Endorsements. We’ll talk about marrying product design and data, deep diving into several of the lessons we’ve learned along the way - all using skills & endorsements as an empirical case study. We’ll include technical detail on our approaches and how we combine crowdsourcing, machine learning, and large scale distributed systems to recommend topics to users.
We’ll also show interesting results on how members are using the endorsements feature and how it’s spread across the network.