mlsanityData to 7 Oct 2026

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

arXiv 2201.10980 ↗First posted 17 Jan 2022cs.IR cs.LGcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 17 Jan 2022.

Builds on

20 of 38 references.

References · 20

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8 more
  1. 2019RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedbackcited by 63
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Citing papers per year: 2023: 1; 2024: 3; 2025: 8; 2026: 1.

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  1. 2025Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modelingcited by 4
  2. 2025The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversioncited by 3
  3. 2024Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systemscited by 2
  4. 2025DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusioncited by 2
  5. 2026IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshucited by 2
  6. 2024Geometric Collaborative Filtering with Convergencecited by 1
  7. 2025AuditAgent: Expert-Guided Multi-Agent Reasoning for Cross-Document Fraudulent Evidence Discoverycited by 1
  8. 2023Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracingcited by 0
  9. 2024Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Predictioncited by 0
  10. 2025Asymmetric Diffusion Recommendation Modelcited by 0
  11. 2025MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Pathscited by 0
  12. 2025On the Mechanisms of Collaborative Learning in VAE Recommenderscited by 0
1 more
  1. 2025LLM-Empowered Representation Learning for Emerging Item Recommendationcited by 0