Modern industrial recommender systems must optimize across competing objectives, balancing semantic relevance with business metrics such as engagement and revenue. While bi-encoders dominate large-scale retrieval due to their efficiency, they collapse these heterogeneous signals into a single static embedding space. Th...
Shao-Bo Zhang, Alice Leung, Yun-Xiang Ren et al.· Proceedings of the 20th ACM...· 0 citations
A unified semantic modeling framework powered by a small language model (SLM) to address the challenges of job understanding in structured and unstructured contexts and provides practical insights into building industry-scale text understanding systems.
Daniel Xu, Baofeng Zheng, Jianqiang Shen et al.· Annual International ACM SIG...· 0 citations
LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR evaluation. Improving the prompts that control these agents is an optimization problem, but in applied IR settings it often looks less like blind search and more like debug...
Derek Koh, Jinghui Mo, Benjamin Le et al.· arXiv.org· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
