# FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

arXiv: https://arxiv.org/abs/2304.04529 · HTML: https://mlsanity.com/paper/2304.04529/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [FeedRec: News Feed Recommendation with Various User Feedbacks](https://mlsanity.com/paper/2102.04903/) (2021)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [Fatigue-aware Bandits for Dependent Click Models](https://mlsanity.com/paper/2008.09733/) (2020)
- [Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://mlsanity.com/paper/2205.06058/) (2022)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios](https://mlsanity.com/paper/2112.13753/) (2021)
- [XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System](https://mlsanity.com/paper/2010.12837/) (2020)
- [Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction](https://mlsanity.com/paper/2204.14069/) (2022)

## Built on by (most cited first, 1 of 1)

- [DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations](https://mlsanity.com/paper/2308.10527/) (2023)

## Abstract (arXiv)

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.