FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation
This work addresses the issue of noisy clicks and user fatigue in e-commerce recommendations, offering a novel method that is incremental but provides strong specific improvements for this domain.
The paper tackles the problem of modeling user fatigue from non-clicks in click-through rate prediction for e-commerce recommendations, proposing FAN which uses Fourier transformation and category modulation to improve performance, with experimental results showing significant gains over existing models in real-world datasets and online tests.
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.