Search Results for author:
Found 8 papers, 1 papers with code
Fairness-Aware Online Personalization
For this purpose, we construct a stylized model for generating training data with potentially biased features as well as potentially biased labels and quantify the extent of bias that is learned by the model when the user responds in a biased manner as in many real-world scenarios.
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice.
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
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 seekers to find suitable career opportunities.
In-Session Personalization for Talent Search
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 a pre-trained model.
Towards Deep and Representation Learning for Talent Search at LinkedIn
In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter.
Towards Data Quality Assessment in Online Advertising
We also present two use cases where we can utilize the data quality assessment results: the first use case is targeting specific user categories, and the second one is forecasting the desirable audiences we can reach for an online advertising campaign with pre-set targeting criteria.
Multi-Touch Attribution Based Budget Allocation in Online Advertising
Budget allocation in online advertising deals with distributing the campaign (insertion order) level budgets to different sub-campaigns which employ different targeting criteria and may perform differently in terms of return-on-investment (ROI).
User Clustering in Online Advertising via Topic Models
In the domain of online advertising, our aim is to serve the best ad to a user who visits a certain webpage, to maximize the chance of a desired action to be performed by this user after seeing the ad.
