# Enhancing CTR Prediction with De-correlated Expert Networks

arXiv: https://arxiv.org/abs/2505.17925 · HTML: https://mlsanity.com/paper/2505.17925/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in MoE for recommender systems, one of 12 directions in Mixture-of-experts.
- First posted on arXiv 23 May 2025.

## Placed in

- Mixture-of-experts / [MoE for recommender systems](https://mlsanity.com/moe/moe-for-recommender-systems/)

## Built on by (most cited first, 7 of 7)

- [Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration](https://mlsanity.com/paper/2604.10150/) (2026)
- [Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation](https://mlsanity.com/paper/2603.07107/) (2026)
- [IE as Cache: Information Extraction Enhanced Agentic Reasoning](https://mlsanity.com/paper/2604.14930/) (2026)
- [Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation](https://mlsanity.com/paper/2605.23191/) (2026)
- [Large Foundation Model for Ads Recommendation](https://mlsanity.com/paper/2508.14948/) (2025)
- [Exploring Test-time Scaling via Prediction Merging on Large-Scale Recommendation](https://mlsanity.com/paper/2512.07650/) (2025)
- [Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking](https://mlsanity.com/paper/2604.15621/) (2026)

## Abstract (arXiv)

Modeling feature interactions is essential for accurate click-through rate (CTR) prediction in advertising systems. Recent studies have adopted the Mixture-of-Experts (MoE) approach to improve performance by ensembling multiple feature interaction experts. These studies employ various strategies, such as learning independent embedding tables for each expert or utilizing heterogeneous expert architectures, to differentiate the experts, which we refer to expert de-correlation. However, it remains unclear whether these strategies effectively achieve de-correlated experts. To address this, we propose a De-Correlated MoE (D-MoE) framework, which introduces a Cross-Expert De-Correlation loss to minimize expert correlations.Additionally, we propose a novel metric, termed Cross-Expert Correlation, to quantitatively evaluate the expert de-correlation degree. Based on this metric, we identify a key finding for MoE framework design: different de-correlation strategies are mutually compatible, and progressively employing them leads to reduced correlation and enhanced performance. Extensive experiments have been conducted to validate the effectiveness of D-MoE and the de-correlation principle. Moreover, online A/B testing on Tencent's advertising platforms demonstrates that D-MoE achieves a significant 1.19% Gross Merchandise Volume (GMV) lift compared to the Multi-Embedding MoE baseline.