Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely on explicit negative user behaviors (e.g., dislikes) or simply treat non-clicks as negative feedback, failing to learn negative user interests comprehensively. In such situations, users may experience fatigue because of seeing too many similar recommendations. In this paper, we propose Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user fatigue from non-clicks. Specifically, we first apply Fourier Transformation to the time series generated from non-clicks, obtaining its frequency spectrum which contains comprehensive information about user fatigue. Then the frequency spectrum is modulated by category information of the target item to model the bias that both the upper bound of fatigue and users' patience is different for different categories. Moreover, a gating network is adopted to model the confidence of user fatigue and an auxiliary task is designed to guide the learning of user fatigue, so we can obtain a well-learned fatigue representation and combine it with user interests for the final CTR prediction. Experimental results on real-world datasets validate the superiority of FAN and online A/B tests also show FAN outperforms representative CTR models significantly.

翻译：由于点击行为通常包含大量噪声，越来越多的研究致力于建模隐式负向用户行为（即非点击行为）。然而，现有方法要么依赖显式负向用户行为（如"不喜欢"），要么简单地将非点击视为负反馈，未能全面学习用户的负向兴趣。在此类场景中，用户可能因看到过多相似推荐而产生疲劳。本文提出疲劳感知网络（FAN），一种直接通过非点击行为感知用户疲劳的新型点击率预测模型。具体而言，首先对非点击行为生成的时间序列进行傅里叶变换，获取包含用户疲劳全面信息的频谱；随后通过目标物品的类别信息对频谱进行调制，以建模不同类别下疲劳上限与用户耐心均存在差异的偏差；此外，采用门控网络建模用户疲劳的置信度，并设计辅助任务引导用户疲劳的学习，从而获得充分学习的疲劳表示，并将其与用户兴趣结合以完成最终点击率预测。在真实数据集上的实验结果验证了FAN的优越性，在线A/B测试也表明FAN显著优于代表性点击率预测模型。