Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange
Recommendation ranking is fundamentally an influence allocation problem: a sorting formula distributes ranking influence among competing factors, and the business outcome depends on finding the optimal "exchange rates" among them. However, offline proxy metrics systematically misjudge how influence reallocation translates to online impact, with asymmetric bias across metrics that a single calibration factor cannot correct. We present Sortify, the first fully autonomous LLM-driven ranking optimization agent deployed in a large-scale production recommendation system. The agent reframes ranking optimization as continuous influence exchange, closing the full loop from diagnosis to parameter d...
| Authors | Yin Cheng, Liao Zhou, Xiyu Liang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 March 2026 |
| Last updated at source | 9 April 2026 |
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