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About this paper
Tao Xue, Yanwu Yang, Panyu Zhai
This review consolidates research on Conversion Rate (CVR) prediction in online advertising by classifying 99 models into six categories, detailing their methodological evolution, performance, and future directions. It provides a structured understanding of techniques from multivariate statistics to deep learning, analyzes their comparative performance, and highlights key challenges like delayed feedback and sample selection bias.
Key insights
- Research in CVR prediction has seen substantial growth, especially from 2017 onwards, driven by advancements in deep learning and industrial demand.
- Deep learning models, particularly those incorporating sequential modeling (RNNs, Transformers), generally outperform traditional statistical and tree-based methods.
- Causal learning, knowledge distillation, and multi-task learning approaches represent key trends and often enhance performance over classical deep learning models by addressing specific CVR challenges.
Summary by alphaXiv.
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