# Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

arXiv: https://arxiv.org/abs/1904.11547 · HTML: https://mlsanity.com/paper/1904.11547/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 30 of 66)

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- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework](https://mlsanity.com/paper/2201.10980/) (2022)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
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- [MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation](https://mlsanity.com/paper/2202.03851/) (2022)
- [BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction](https://mlsanity.com/paper/2308.11527/) (2023)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems](https://mlsanity.com/paper/2108.13592/) (2021)
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- [VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results](https://mlsanity.com/paper/2509.02969/) (2025)
- [A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation](https://mlsanity.com/paper/2112.02275/) (2021)
- [VFed-SSD: Towards Practical Vertical Federated Advertising](https://mlsanity.com/paper/2205.15987/) (2022)
- [Nonparametric Embeddings of Sparse High-Order Interaction Events](https://mlsanity.com/paper/2207.03639/) (2022)
- [Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2208.09130/) (2022)
- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems](https://mlsanity.com/paper/2401.04338/) (2024)
- [Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model](https://mlsanity.com/paper/2405.05606/) (2024)
- [Delving Deep into Engagement Prediction of Short Videos](https://mlsanity.com/paper/2410.00289/) (2024)
- [Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling](https://mlsanity.com/paper/2504.06270/) (2025)

## Abstract (arXiv)

Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representations of ad IDs drastically improve CTR prediction accuracies. However, such learning techniques are data demanding and work poorly on new ads with little logging data, which is known as the cold-start problem. In this paper, we aim to improve CTR predictions during both the cold-start phase and the warm-up phase when a new ad is added to the candidate pool. We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs. The proposed method trains an embedding generator for new ad IDs by making use of previously learned ads through gradient-based meta-learning. In other words, our method learns how to learn better embeddings. When a new ad comes, the trained generator initializes the embedding of its ID by feeding its contents and attributes. Next, the generated embedding can speed up the model fitting during the warm-up phase when a few labeled examples are available, compared to the existing initialization methods. Experimental results on three real-world datasets showed that Meta-Embedding can significantly improve both the cold-start and warm-up performances for six existing CTR prediction models, ranging from lightweight models such as Factorization Machines to complicated deep models such as PNN and DeepFM. All of the above apply to conversion rate (CVR) predictions as well.