Ainur Yessenalina

Job postings are critical for recruitment, yet large enterprises struggle with standardization and consistency, requiring significant time and effort from hiring managers and recruiters. We present a feedback-aware prompt optimization framework that automates high-quality job posting generation through iterative human-in-the-loop refinement.

Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, users’ viewership patterns/statistics can drastically change from one geographic location to another which may suggest to learn specific user embeddings.