发表机构
Ant Group（蚂蚁集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究首页营销板块推荐中访问习惯干扰问题，提出OrDA框架，用双塔结构、门控分配层、正交正则化及因果干预净化兴趣信号，有效消除访问习惯偏差，提升预测准确性和用户点击率。
AI 中文摘要
首页营销板块的点击由内容兴趣和访问习惯的双重机制驱动。然而，习惯性点击常导致营销位出现伪阳性，位置优势掩盖了平庸的内容质量，导致推荐生态系统有偏差。我们提出了一种名为访问习惯正交解缠（OrDA）的框架来净化兴趣信号。OrDA采用带有门控分配层的双塔结构来自适应路由特征并最小化干扰。为确保严格分离，我们采用正交正则化来约束潜在兴趣和习惯流形在几何上垂直。OrDA在推理期间进行因果干预（do-演算），仅根据净化后的兴趣分数对项目进行排名。在大规模数据集上的实证在线评估表明，OrDA有效地消除了访问习惯偏差，在预测准确性方面优于现有方法。在线AB测试显示，在芝麻首页营销板块、芝麻租房楼层推荐上用户点击率（UCTR）提高了5.64%。
英文摘要
Clicks on homepage marketing blocks are driven by a dual-mechanism of content interest and access habits. However, habitual clicks often create Pseudo-Positives in marketing slots, where position advantage masks mediocre content quality, leading to biased recommendation ecosystems. We propose a framework called Orthogonal Disentanglement of Access habits (OrDA) to purify interest signals. OrDA utilizes a dual-tower structure with a gated allocation layer to adaptively route features and minimize interference. To ensure rigorous separation, we employ orthogonal regularization to constrain the latent interest and habit manifolds to be geometrically perpendicular. OrDA performs causal intervention (do-calculus) during inference to rank items solely by purified interest scores. Empirical online evaluations on large-scale datasets demonstrate that OrDA effectively eliminates access-habit bias, outperforming state-of-the-art methods in predictive accuracy. Online AB test 5.64% shows user click-through rates (UCTR) improvement on the Zhima homepage marketing block, Zhima rent-floor recommendation.
