融合文本与点击数据，实现广告点击量精准预测与可解释输出。
Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
- 结合点击数据与广告文本日志，多模态输入提升预测能力。
- 在大规模工业数据集上，预测准确率优于基线方法。
- 生成人类可理解的解释，适合需要透明决策的广告从业者。
点击量预测是数字广告中的关键任务，直接影响收入与投放策略。传统时间序列模型仅依赖数值数据，常忽略文本信息（如关键词更新）中的丰富上下文。本文提出一种多模态预测框架，融合真实广告活动的点击数据与文本日志，生成数值预测的同时输出人类可解释的推理说明。采用强化学习提升对文本信息的理解，并优化多模态融合效果。在大规模工业数据集上的实验表明，该方法在预测准确率与推理质量上均优于基线模型。
原文摘要 · Abstract (English)
Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often overlooking rich contextual information embedded in textual elements, such as keyword updates. We present a multimodal forecasting framework that combines click data with textual logs from real-world ad campaigns and generates human-interpretable explanations alongside numeric predictions. Reinforcement learning is used to improve comprehension of textual information and enhance fusion of modalities. Experiments on a large-scale industry dataset show that our method outperforms baselines in both accuracy and reasoning quality.
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