At LinkedIn, our data scientists and researchers have the opportunity to work with massive amounts of data, solve real problems for our members around the world, and publish at major conferences. They work to improve the relevance of our products, contribute to the open source community, and routinely produce outstanding research. If you’re a regular reader of this blog, you already know about the outstanding research done by LinkedIn’s Analytics, Data Science, and Relevance teams.
Occasionally, though, we are lucky enough to get to highlight a researcher whose work is recognized by their peers as being truly exceptional. Today it is my pleasure to recognize two awards given to one such member of our team.
Research that stands the test of time
Badrul Sarwar is a member of the Relevance team that builds and designs the recommendation algorithms, models, and systems that power key LinkedIn products like Sales Navigator. Twice in as many years, Badrul has been recognized for his exceptional research in the field of recommender systems.
This year, along with his co-authors, he received the 2017 AAAI Classic Paper (Honorable Mention) for their 1999 paper “Combining Collaborative Filtering with Personal Agents for Better Recommendations.” This work shows the benefit of combining community recommendations with personalized agents in recommender systems, and has had a large impact on the way researchers and engineers now think about recommendations.
This award was established to honor the authors of papers deemed most influential in a specific conference year, and it has been shared over the years by many well-known AI researchers. Prior winners and honorable mentions include Judea Pearl, Eugene Charniak, and Sebastian Thrun. The 2017 award and honorable mentions were given for the most influential papers from the Sixteenth National Conference on Artificial Intelligence (1999).
In 2016, Badrul and his colleagues also had the distinction of being awarded the 2016 Seoul Test of Time Award for their seminal 2001 paper titled “Item-Based Collaborative Filtering Recommendation Algorithms.” This paper has had considerable real-world impact and is the basis for many of the collaborative filtering systems in use today. If you’ve ever gone online to purchase anything or to look for a movie recommendation, odds are that the service you used was influenced by this paper! It has over 6,200 citations in Google Scholar, and it first crossed over into mainstream public awareness when it was the subject of its own feature article in The Economist.
The Seoul Test of Time Award recognizes the most significant papers published in the history of the International World Wide Web (WWW) Conference series. It was introduced at WWW2015 and first presented to Google co-founders Larry Page and Sergey Brin for their 1998 work “The Anatomy of a Large-Scale Hypertextual Web Search Engine.” I am sure that Badrul and his co-authors are very proud to be in such company.
Great job Badrul!
Supporting public-private research partnerships
In order to further work of this caliber, LinkedIn strongly supports partnerships between academia and the tech industry. I would like to take this opportunity to highlight two upcoming opportunities for researchers.
AI Tech Talk program: On May 18 at the LinkedIn Sunnyvale campus, we will be kicking off our AI Tech Talk event series, sponsored by Vice President of Engineering Deepak Agarwal, Head of Relevance and Artificial Intelligence at LinkedIn. The goal behind this program is to bring local scientists and engineering leaders together to learn and discuss trends and technical innovations in the AI and machine learning space. Our first tech talk will be focused on the current and future impact of machine learning to create economic opportunities for the global workforce and make professionals more productive. Interested researchers should RSVP here.
Economic Graph Research program: Two years ago, we launched an ambitious project with a simple idea: there are approximately 3 billion people in the global workforce, and LinkedIn’s vision is to create economic opportunity for every one of them. By mapping economic relationships and developing the world’s first Economic Graph, we believe we can make that vision a reality. What projects could researchers propose that have the potential to create greater economic opportunity? This year, we’ve broken down the research topics into three major areas with the following broad categories: analytics, economics, and artificial intelligence. You can learn more about the EGR Call for Proposals (deadline June 1) here.