296 citations · 355 across the 7 of their papers we have counts for
7 papers · 1 filter
Fairness-Aware Online Personalization
G Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi
Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluenc…
Entity Personalized Talent Search Models with Tree Interaction Features
Cagri Ozcaglar, Sahin Geyik, Brian Schmitz +4
Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job postin…
In-Session Personalization for Talent Search
Sahin Cem Geyik, Vijay Dialani, Meng Meng +1
Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing…
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
Sahin Cem Geyik, Qi Guo, Bo Hu +4
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job see…
Towards Data Quality Assessment in Online Advertising
Sahin Cem Geyik, Jianqiang Shen, Shahriar Shariat +2
In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data s…
Multi-Touch Attribution Based Budget Allocation in Online Advertising
Sahin Cem Geyik, Abhishek Saxena, Ali Dasdan
Budget allocation in online advertising deals with distributing the campaign (insertion order) level budgets to different sub-campaigns which employ different targeting criteria an…