Recruiters, Job Seekers and Spammers: Innovations in
                  Job Search at LinkedIn

                                                   Daria Sorokina
                                               dsorokina@linkedin.com
                                                          LinkedIn




Keynote Abstract
In this talk I will share insights about how LinkedIn responds to the needs of two groups of users - recruiters
and job seekers, while making life more difficult for a third group - search engine optimization (SEO) spammers.
   When recruiters search for good candidates, they are primarily interested in candidates whose profiles match
the descriptions of a particular job posting. However, a simple textual match with the job requirements is often
not enough. We will discuss other signals that can help recruiters to achieve their goals along with the ways we
combine the signals from multiple models.
   Professional search at LinkedIn is highly personalized. Regardless of what they enter into the search box, job
seekers prefer to find job postings whose requirements match their skills and generally are more interested in
opportunities near where they currently live. This personalization makes standard crowdsourced labeling of the
training data complicated - it is hard for a random labeler to imagine which job ad a particular user would find
relevant. As a result, our job search models have to rely primarily on signals from user activity.
   Being ranked high in the results for common queries on LinkedIn search can bring new clients, business
connections, or job offers. As as a result, some users create profiles stuffed with keywords, fake entries and
other junk information in order to raise their search ranking. While we can not share specific details on how we
identify such users, I will present the general machine learning framework along with examples of difficult cases
and unusual spammer techniques we have encountered.

About the speaker
Daria Sorokina is Senior Data Scientist at LinkedIn, who enjoys working with large complex data sets. For
the last few years she has been specializing in search, before that she worked on a variety of machine learning
applications in different domains. Daria is the author of Additive Groves, the best off-the-shelf machine learning
algorithm for a variety of tasks.




          c by the paper’s authors. Copying permitted only for private and academic purposes.
Copyright !
In: M. Lupu, M. Salampasis, N. Fuhr, A. Hanbury, B. Larsen, H. Strindberg (eds.): Proceedings of the Integrating IR technologies
for Professional Search Workshop, Moscow, Russia, 24-March-2013, published at http://ceur-ws.org



                                                          7