# RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction

arXiv: https://arxiv.org/abs/2210.16080 · HTML: https://mlsanity.com/paper/2210.16080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 28 Oct 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [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)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [A Survey of Deep Meta-Learning](https://mlsanity.com/paper/2010.03522/) (2020)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [Pooling Methods in Deep Neural Networks, a Review](https://mlsanity.com/paper/2009.07485/) (2020)
- [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://mlsanity.com/paper/1909.03276/) (2019)
- [CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network](https://mlsanity.com/paper/2005.10549/) (2020)
- [MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation](https://mlsanity.com/paper/2007.03183/) (2020)
- [Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks](https://mlsanity.com/paper/2105.04790/) (2021)
- [From Zero-Shot Learning to Cold-Start Recommendation](https://mlsanity.com/paper/1906.08511/) (2019)
- [Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild](https://mlsanity.com/paper/1505.03014/) (2015)
- [A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users](https://mlsanity.com/paper/2007.15293/) (2020)
- [Content-aware Neural Hashing for Cold-start Recommendation](https://mlsanity.com/paper/2006.00617/) (2020)
- [Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste](https://mlsanity.com/paper/2007.13287/) (2020)
- [Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1707.08105/) (2017)
- [Joint Training Capsule Network for Cold Start Recommendation](https://mlsanity.com/paper/2005.11467/) (2020)
- [Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation](https://mlsanity.com/paper/2003.10719/) (2020)

## Abstract (arXiv)

Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based meta-learning. However, existing methods suffer from information loss or inefficient optimization process, and they fail to explicitly model global user preference knowledge which is crucial to complement the sparse and insufficient preference information of cold users. In this paper, we propose a novel and efficient approach named RESUS, which decouples the learning of global preference knowledge contributed by collective users from the learning of residual preferences for individual users. Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users. Meanwhile, we develop two efficient algorithms based on the nearest neighbor and ridge regression predictors, which infer residual user preferences via learning quickly from a few user-specific interactions. Extensive experiments on three public datasets demonstrate that our RESUS approach is efficient and effective in improving CTR prediction accuracy on cold users, compared with various state-of-the-art methods.