# ColdNAS: Search to Modulate for User Cold-Start Recommendation

arXiv: https://arxiv.org/abs/2306.03387 · HTML: https://mlsanity.com/paper/2306.03387/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 29 of 29)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018)
- [Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes](https://mlsanity.com/paper/1906.07697/) (2019)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Evaluating the Search Phase of Neural Architecture Search](https://mlsanity.com/paper/1902.08142/) (2019)
- [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](https://mlsanity.com/paper/1906.12192/) (2019)
- [AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.11235/) (2020)
- [MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation](https://mlsanity.com/paper/2007.03183/) (2020)
- [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction](https://mlsanity.com/paper/2007.06434/) (2020)
- [ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding](https://mlsanity.com/paper/2010.06176/) (2020)
- [Task-adaptive Neural Process for User Cold-Start Recommendation](https://mlsanity.com/paper/2103.06137/) (2021)
- [Efficient Neural Architecture Search via Proximal Iterations](https://mlsanity.com/paper/1905.13577/) (2019)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [Efficient Neural Interaction Function Search for Collaborative Filtering](https://mlsanity.com/paper/1906.12091/) (2019)
- [Personalized Adaptive Meta Learning for Cold-start User Preference Prediction](https://mlsanity.com/paper/2012.11842/) (2020)
- [FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data](https://mlsanity.com/paper/2007.14573/) (2020)
- [Bilinear Scoring Function Search for Knowledge Graph Learning](https://mlsanity.com/paper/2107.00184/) (2021)
- [CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation](https://mlsanity.com/paper/2108.10511/) (2021)
- [Efficient Data-specific Model Search for Collaborative Filtering](https://mlsanity.com/paper/2106.07453/) (2021)

## Built on by (most cited first, 4 of 4)

- [PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching](https://mlsanity.com/paper/2506.18382/) (2025)
- [LLM-Empowered Representation Learning for Emerging Item Recommendation](https://mlsanity.com/paper/2512.10370/) (2025)
- [Distillation-based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendation](https://mlsanity.com/paper/2601.02368/) (2025)
- [UNISON: A Unified Framework for Learning from Scored Bags](https://mlsanity.com/paper/2610.15564/) (2026)

## Abstract (arXiv)

Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypernetworks to directly map user interaction histories to user-specific parameters, which are then used to modulate predictor by feature-wise linear modulation function. These works obtain the state-of-the-art performance. However, the physical meaning of scaling and shifting in recommendation data is unclear. Instead of using a fixed modulation function and deciding modulation position by expertise, we propose a modulation framework called ColdNAS for user cold-start problem, where we look for proper modulation structure, including function and position, via neural architecture search. We design a search space which covers broad models and theoretically prove that this search space can be transformed to a much smaller space, enabling an efficient and robust one-shot search algorithm. Extensive experimental results on benchmark datasets show that ColdNAS consistently performs the best. We observe that different modulation functions lead to the best performance on different datasets, which validates the necessity of designing a searching-based method.