LinkedIn Endorsements: Reputation, Virality, and Social Tagging

Connected World Ballroom F
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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:

1. How do you automatically create a taxonomy of skills in the professional context?

2. How do you disambiguate between different contexts of skills? For instance, “search” could mean information retrieval, search & seizure, search & rescue, among others.

3. How can you leverage data to determine someone’s authoritativeness in a skill?

4. How do you use that authoritativeness to recommend people to endorse?

5. 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.

Photo of Sam Shah

Sam Shah


Sam Shah is a principal engineer on the LinkedIn data team. He leads many of the site’s large-scale recommendation and analytics systems, which analyze hundreds of terabytes of data daily to produce products and insights that serve LinkedIn’s members. His work involves pure research, product-focused features, and infrastructure development, including social network analysis, recommendation engines, distributed systems, and grid computing. Some of the products under his purview include “People You May Know”, “Who’s Viewed My Profile?”, Skills & Endorsements, related searches, job recommendations, and more. Sam holds a Ph.D. in Computer Science from the University of Michigan.

Photo of Pete Skomoroch

Pete Skomoroch


Pete Skomoroch is a Principal Data Scientist at LinkedIn where he leads a team focused on identity, reputation, information extraction, and building data driven products. He was also the creator of LinkedIn Skills

Prior to LinkedIn, he was based in Washington, DC where he mined insights from search query data as the Director of Advanced Analytics at Juice Analytics and as a Sr. Research Engineer at AOL Search. While in DC, he also founded, which provided custom data mining solutions to clients in bioinformatics, finance, and cloud computing.

He spent the previous 6 years in Boston implementing Biodefense pattern detection algorithms for streaming sensor data at MIT Lincoln Laboratory and constructing predictive models for large retail datasets at Profitlogic (now Oracle Retail).

Pete has a B.S. in Mathematics and Physics from Brandeis University and did graduate coursework in machine learning at MIT.

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Picture of Sam Shah
Sam Shah
03/04/2013 1:39am PST

The slides are available here:


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