# Feature Staleness Aware Incremental Learning for CTR Prediction

arXiv: https://arxiv.org/abs/2505.02844 · HTML: https://mlsanity.com/paper/2505.02844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [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)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/1910.05552/) (2019)
- [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://mlsanity.com/paper/1909.03276/) (2019)
- [How to Retrain Recommender System? A Sequential Meta-Learning Method](https://mlsanity.com/paper/2005.13258/) (2020)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces](https://mlsanity.com/paper/2211.17068/) (2022)
- [GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation](https://mlsanity.com/paper/2007.02747/) (2020)
- [ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation](https://mlsanity.com/paper/2007.12000/) (2020)
- [Real-Time Bidding Benchmarking with iPinYou Dataset](https://mlsanity.com/paper/1407.7073/) (2014)
- [Streaming Recommender Systems](https://mlsanity.com/paper/1607.06182/) (2016)
- [A Practical Incremental Method to Train Deep CTR Models](https://mlsanity.com/paper/2009.02147/) (2020)
- [Memory Augmented Neural Model for Incremental Session-based Recommendation](https://mlsanity.com/paper/2005.01573/) (2020)
- [Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems](https://mlsanity.com/paper/2111.04282/) (2021)
- [An Incremental Learning framework for Large-scale CTR Prediction](https://mlsanity.com/paper/2209.00458/) (2022)

## Abstract (arXiv)

Click-through Rate (CTR) prediction in real-world recommender systems often deals with billions of user interactions every day. To improve the training efficiency, it is common to update the CTR prediction model incrementally using the new incremental data and a subset of historical data. However, the feature embeddings of a CTR prediction model often get stale when the corresponding features do not appear in current incremental data. In the next period, the model would have a performance degradation on samples containing stale features, which we call the feature staleness problem. To mitigate this problem, we propose a Feature Staleness Aware Incremental Learning method for CTR prediction (FeSAIL) which adaptively replays samples containing stale features. We first introduce a staleness aware sampling algorithm (SAS) to sample a fixed number of stale samples with high sampling efficiency. We then introduce a staleness aware regularization mechanism (SAR) for a fine-grained control of the feature embedding updating. We instantiate FeSAIL with a general deep learning-based CTR prediction model and the experimental results demonstrate FeSAIL outperforms various state-of-the-art methods on four benchmark datasets.