用多模态大模型主动识别季节性广告，提升转化率与用户体验。
Proactive Detection and Calibration of Seasonal Advertisements with Multimodal Large Language Models
- 采用多模态大模型检测广告季节性特征
- 在自建基准上达0.97的最高F1分数
- 适合广告系统优化与推荐算法研究者
大规模广告投放系统受多种因素影响，进而影响用户体验与收益。其中，主动检测并校准季节性广告是提升转化率与用户满意度的关键。本文提出主动检测与校准季节性广告（PDCaSA）问题，基于真实工业级广告排序系统的实践，提供问题定义、动机、评估指标，并揭示数据标注与机器学习建模中的挑战、经验教训与最佳实践。最终方案采用多模态大模型（MLMs）进行季节性检测，在自研基准上取得0.97的顶峰F1得分。研究展望将MLMs作为知识蒸馏的教师、机器标签生成器及集成分层检测系统的一部分，以增强广告排序系统的季节性感知能力。
原文摘要 · Abstract (English)
A myriad of factors affect large scale ads delivery systems and influence both user experience and revenue. One such factor is proactive detection and calibration of seasonal advertisements to help with increasing conversion and user satisfaction. In this paper, we present Proactive Detection and Calibration of Seasonal Advertisements (PDCaSA), a research problem that is of interest for the ads ranking and recommendation community, both in the industrial setting as well as in research. Our paper provides detailed guidelines from various angles of this problem tested in, and motivated by a large-scale industrial ads ranking system. We share our findings including the clear statement of the problem and its motivation rooted in real-world systems, evaluation metrics, and sheds lights to the existing challenges, lessons learned, and best practices of data annotation and machine learning modeling to tackle this problem. Lastly, we present a conclusive solution we took during this research exploration: to detect seasonality, we leveraged Multimodal LLMs (MLMs) which on our in-house benchmark achieved 0.97 top F1 score. Based on our findings, we envision MLMs as a teacher for knowledge distillation, a machine labeler, and a part of the ensembled and tiered seasonality detection system, which can empower ads ranking systems with enriched seasonal information.
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