AI 中文总结
研究用户与排名交互中点击概率建模问题，提出基于证据深度学习的方法，输出贝塔分布捕捉认知不确定性，经实验验证该方法有效，是将贝叶斯不确定性纳入点击建模的重要进展。
AI 中文摘要
用户与排名的交互受项目相关性和显示位置的影响。因此，点击概率常被建模为相关性和位置因素的乘积，为改进推荐和搜索，需区分相关性与位置偏差。现有点击模型仅提供频率主义点估计，无法捕捉认知不确定性。本文引入首个证据深度学习方法，形成基于位置的重要点击模型的认知替代方案。学习模型以项目和位置特征为输入，为基于位置模型的每个相关性和位置偏差变量输出贝塔分布，捕捉点击概率的认知不确定性及吸引力和位置偏差的潜在影响。主要挑战是优化，为此提出近似和条件技术以提供数值稳定性和方差减少。实验表明该方法能捕捉未见数据预测中的认知不确定性，而标准策略梯度无法学习有意义的分布。我们认为首个上下文认知点击模型的贡献是将贝叶斯不确定性纳入点击建模的重要一步。
英文摘要
User interactions with rankings are affected by both items' relevances and display positions. Accordingly, click probabilities are often modeled as a product of relevance and position factors; and for improving recommendation and search, one needs to disentangle relevance from position bias. However, existing click models only provide frequentist point-estimates that do not capture any measure of epistemic uncertainty. Consequently, there is no indication of how much confidence one should have in their predictions. In this work, we introduce the first evidential deep-learning approach to form an epistemic alternative to the important position-based click model. Our learned model takes as input item and position features and outputs a beta-distribution for every relevance and position-bias variable of the position-based model. These distributions capture epistemic uncertainty about click probabilities and the underlying effects of attraction and position bias. The main challenge of our approach is its optimization for which we propose approximation and conditioning techniques to provide numerical stability and variance reduction. Our experiments indicate that our approach captures epistemic uncertainty in predictions on previously-unseen data, whereas standard policy gradients fail to learn meaningful distributions. We believe our contribution of the first contextual epistemic click model constitutes an important step in incorporating Bayesian uncertainty into click modeling.
CommentsPublished at SIGIR 2026
