Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction
arXiv:2605.24986v1 Announce Type: cross Abstract: Generative pre-training via discrete diffusion provides dense reconstruction supervision across all feature fields simultaneously, mitigating representation collapse from data sparsity in CTR prediction. However, all existing generative CTR methods share a fundamental limitation: the reconstruction objective assigns equal training weight to every feature field, ignoring the profound heterogeneity of reconstruction difficulty across high-cardinality ID fields, sparse categorical attributes, numerical values, and behavioral sequences. This causes
The increasing complexity and scale of Click-Through Rate (CTR) prediction models, alongside the inherent heterogeneity of data, necessitate more sophisticated generative pre-training methods to overcome limitations like data sparsity and representation collapse.
Improving CTR prediction accuracy directly impacts revenue for ad platforms, e-commerce, and recommendation systems, making advances in this area critical for digital economies.
This research introduces a method to address a fundamental limitation in generative CTR models by assigning differentiated training weights to various feature fields, allowing for more robust and accurate predictions.
- · Adtech platforms
- · E-commerce companies
- · Recommendation system providers
- · Data scientists specializing in deep learning
- · Companies relying on less sophisticated CTR models
- · Generic generative model approaches
More efficient and accurate ad targeting and content recommendations will become possible.
Increased ad revenue and user engagement for platforms implementing these advanced CTR prediction techniques.
A potential shift towards more personalized and less intrusive user experiences as prediction accuracy improves, leading to higher user satisfaction and retention.
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Read at arXiv cs.LG