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