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.

本文提出了一种新的CTR模型——疲劳感知网络(Fatigue-Aware Network，FAN)，可以直接从用户的非点击行为中感知疲劳。通过将离散傅里叶变换应用于非点击生成的时间序列，获取用户疲劳的全面信息，进而结合目标项的类别信息来模拟用户疲劳的上限和耐心不同的偏见，并采用一种门控网络来模拟用户的疲劳信心。通过实验结果表明，该模型在真实数据集上具有优越性，并且在线A/B测试也显示FAN明显优于CTR模型。

FAN: 电子商务推荐中基于疲劳感知的点击率预测网络