# Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

arXiv: https://arxiv.org/abs/2201.10980 · HTML: https://mlsanity.com/paper/2201.10980/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [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)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [An Embedding Learning Framework for Numerical Features in CTR Prediction](https://mlsanity.com/paper/2012.08986/) (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)
- [Sequential Scenario-Specific Meta Learner for Online Recommendation](https://mlsanity.com/paper/1906.00391/) (2019)
- [Dual Graph enhanced Embedding Neural Network for CTR Prediction](https://mlsanity.com/paper/2106.00314/) (2021)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [A Hybrid Variational Autoencoder for Collaborative Filtering](https://mlsanity.com/paper/1808.01006/) (2018)
- [Joint Variational Autoencoders for Recommendation with Implicit Feedback](https://mlsanity.com/paper/2008.07577/) (2020)

## Built on by (most cited first, 13 of 13)

- [Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling](https://mlsanity.com/paper/2504.06270/) (2025)
- [The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion](https://mlsanity.com/paper/2505.05605/) (2025)
- [Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems](https://mlsanity.com/paper/2405.13238/) (2024)
- [DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion](https://mlsanity.com/paper/2508.14500/) (2025)
- [IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu](https://mlsanity.com/paper/2603.01590/) (2026)
- [Geometric Collaborative Filtering with Convergence](https://mlsanity.com/paper/2410.03064/) (2024)
- [AuditAgent: Expert-Guided Multi-Agent Reasoning for Cross-Document Fraudulent Evidence Discovery](https://mlsanity.com/paper/2510.00156/) (2025)
- [Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing](https://mlsanity.com/paper/2309.01179/) (2023)
- [Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction](https://mlsanity.com/paper/2410.15098/) (2024)
- [Asymmetric Diffusion Recommendation Model](https://mlsanity.com/paper/2508.12706/) (2025)
- [MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths](https://mlsanity.com/paper/2510.08932/) (2025)
- [On the Mechanisms of Collaborative Learning in VAE Recommenders](https://mlsanity.com/paper/2511.06781/) (2025)
- [LLM-Empowered Representation Learning for Emerging Item Recommendation](https://mlsanity.com/paper/2512.10370/) (2025)

## Abstract (arXiv)

We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probabilistic embedding, and incorporating trainable and regularized priors which utilize the rich side information of cold start users and advertisements (Ads). The two techniques are naturally integrated into a variational inference framework, forming an end-to-end training process. Abundant empirical tests on benchmark datasets well demonstrate the advantages of our proposed VELF. Besides, extended experiments confirmed that our parameterized and regularized priors provide more generalization capability than traditional fixed priors.