PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems
arXiv:2607.08202v1 Announce Type: new Abstract: Estimating original-space conditional expectations is central to value-driven recommender systems, including dwell time, GMV, and LTV forecasting. Standard MSE is expectation-consistent in principle, but its gradients become unstable on heavy-tailed, zero-inflated, and multimodal targets, causing mean collapse and tail shrinkage. Target transformation alleviates this scale conflict, yet any useful nonlinear marginal transform loses expectation consistency under direct inversion. This is not an implementation oversight: a direct inverse-transform
The paper addresses a long-standing challenge in recommender systems where traditional methods struggle with complex data distributions, a problem becoming more acute with the scale and diversity of modern datasets.
This research provides a fundamental improvement in the accuracy and stability of value-driven recommender systems, which are critical for revenue generation and user experience in large-scale online platforms.
Recommender systems can now achieve more stable and expectation-consistent predictions for metrics like dwell time and GMV, leading to better optimization and potentially higher conversion rates.
- · Large e-commerce platforms
- · Streaming services
- · Adtech companies
- · Data scientists and ML engineers
- · Companies relying on less sophisticated recommendation engines
Improved model performance and stability in recommenders across various industries.
Enhanced user engagement and monetization for platforms deploying these advanced techniques.
Increased competitive pressure on companies that do not adopt similar robust expectation-consistent methodologies.
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Read at arXiv cs.LG