mlsanityData to 14 Oct 2026

Click-through Rate Prediction with Auto-Quantized Contrastive Learning

arXiv 2109.13921 ↗First posted 27 Sep 2021cs.IR cs.AI cs.LGcited by 7

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Abstract (arXiv)

Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scenarios where the user interactions are extremely sparse. We consider this problem as an automatic identification about whether the user behaviors are rich enough to capture the interests for prediction, and propose an Auto-Quantized Contrastive Learning (AQCL) loss to regularize the model. Different from previous methods, AQCL explores both the instance-instance and the instance-cluster similarity to robustify the latent representation, and automatically reduces the information loss to the active users due to the quantization. The proposed framework is agnostic to different model architectures and can be trained in an end-to-end fashion. Extensive results show that it consistently improves the current state-of-the-art CTR models.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 49 of its 69 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

49 of 69 references.

References · 49

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37 more
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 0; 2025: 2.

7 papers.

  1. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  2. 2022CL4CTR: A Contrastive Learning Framework for CTR Predictioncited by 14
  3. 2025Distinguished Quantized Guidance for Diffusion-based Sequence Recommendationcited by 13
  4. 2022Device-Cloud Collaborative Recommendation via Meta Controllercited by 10
  5. 2023MAP: A Model-agnostic Pretraining Framework for Click-through Rate Predictioncited by 10
  6. 2025PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendationcited by 2
  7. 2023Contrastive Multi-view Framework for Customer Lifetime Value Predictioncited by 1

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  1. 2022An Incremental Learning framework for Large-scale CTR Prediction
  2. 2022Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
  3. 2025Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
  4. 2024Retrieval-Oriented Knowledge for Click-Through Rate Prediction
  5. 2024RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
  6. 2025DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
  7. 2023UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
  8. 2021Dynamic Parameterized Network for CTR Prediction