用鼠标轨迹精准量化搜索页多广告位用户注意力。
AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search
- 基于轨迹序列的Transformer模型，融合位置特征预测注意力。
- 在固定时间与点击次数预测上准确率显著提升。
- 适合广告优化与网页布局设计的研究者和从业者。
现代搜索引擎结果页（SERPs）呈现复杂布局，多个元素竞争用户注意力。注意力建模对优化网页设计和计算广告至关重要，注意力指标可指导广告位放置与收益策略。我们提出AdSight，一种利用鼠标轨迹实现多广告位环境下的可扩展、高精度用户注意力量化方法。AdSight采用新型Transformer序列到序列架构，编码器处理鼠标轨迹嵌入，解码器融合槽位特异性特征，可在不同SERP布局下实现稳健的注意力预测。我们在两个机器学习任务上评估该方法：(1) 回归任务，预测注视时长与次数；(2) 分类任务，判断某些槽位是否被注意到。结果表明，该模型在注意力预测上达到前所未有的精度，为研究者与实践者提供可操作的洞察。
原文摘要 · Abstract (English)
Modern Search Engine Results Pages (SERPs) present complex layouts where multiple elements compete for visibility. Attention modelling is crucial for optimising web design and computational advertising, whereas attention metrics can inform ad placement and revenue strategies. We introduce AdSight, a method leveraging mouse cursor trajectories to quantify in a scalable and accurate manner user attention in multi-slot environments like SERPs. AdSight uses a novel Transformer-based sequence-to-sequence architecture where the encoder processes cursor trajectory embeddings, and the decoder incorporates slot-specific features, enabling robust attention prediction across various SERP layouts. We evaluate our approach on two Machine Learning tasks: (1) regression, to predict fixation times and counts; and (2) classification, to determine some slot types were noticed. Our findings demonstrate the model's ability to predict attention with unprecedented precision, offering actionable insights for researchers and practitioners.
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