# Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study

arXiv: https://arxiv.org/abs/2607.27577 · HTML: https://mlsanity.com/paper/2607.27577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MoE for recommender systems, one of 12 directions in Mixture-of-experts.
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [A Review of Sparse Expert Models in Deep Learning](https://mlsanity.com/paper/2209.01667/) (2022)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [Merging Multi-Task Models via Weight-Ensembling Mixture of Experts](https://mlsanity.com/paper/2402.00433/) (2024)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](https://mlsanity.com/paper/1906.12192/) (2019)
- [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://mlsanity.com/paper/2302.01115/) (2023)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric](https://mlsanity.com/paper/1902.05826/) (2019)
- [M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework](https://mlsanity.com/paper/2404.18465/) (2024)
- [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022)
- [Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems](https://mlsanity.com/paper/2006.04279/) (2020)
- [Recommender Systems with Heterogeneous Side Information](https://mlsanity.com/paper/1907.08679/) (2019)
- [Personalizing Fairness-aware Re-ranking](https://mlsanity.com/paper/1809.02921/) (2018)
- [Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching](https://mlsanity.com/paper/2111.00926/) (2021)
- [Preserving AUC Fairness in Learning with Noisy Protected Groups](https://mlsanity.com/paper/2505.18532/) (2025)
- [Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation](https://mlsanity.com/paper/2405.12892/) (2024)
- [Deep Unified Representation for Heterogeneous Recommendation](https://mlsanity.com/paper/2201.05861/) (2022)
- [Representational Alignment Across Model Layers and Brain Regions with Multi-Level Optimal Transport](https://mlsanity.com/paper/2510.01706/) (2025)
- [Distributionally-Informed Recommender System Evaluation](https://mlsanity.com/paper/2309.05892/) (2023)
- [A Parameter Update Balancing Algorithm for Multi-task Ranking Models in Recommendation Systems](https://mlsanity.com/paper/2410.05806/) (2024)

## Abstract (arXiv)

Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.g., music-only or video-only closed-ecosystem platforms). In Google Discover, a unified feed integrates diverse content sourced from the decentralized open web, including web articles, long-form and short-form videos, user-generated content (UGC), and beyond. Different content types exhibit distinct feature densities and user interaction patterns. Building a unified ranking model that sustains high performance across such heterogeneity, while avoiding negative transfer or majority bias, remains a significant industrial challenge. This paper presents an end-to-end case study on the industrial-scale multi-task ranking of heterogeneous feeds, grounded in real-world deployment. We introduce HA-MoE, a heterogeneity-adaptive multi-gated mixture-of-experts architecture that incorporates explicit heterogeneity context into both gating networks and expert representations. This approach enables effective specialization without significantly increasing operational overhead. To support reliable deployment, we introduce LENS, a lightweight observability framework that provides interpretable diagnostics of expert specialization and tracks this functional heterogeneity across continuous retraining. We evaluate our method using Dual-Level AUC (DL-AUC), a heterogeneity-aware evaluation metric that combines global ranking performance with cross-segment ranking correctness. Offline evaluations on a large-scale industrial dataset demonstrate consistent improvements over baseline models. Furthermore, online A/B testing confirms gains in feed activity and exploration metrics. Together, offline and online results validate the effectiveness of our approach for managing heterogeneity in industrial-scale recommender systems.