# Adaptive Conditional Expert Selection Network for Multi-domain Recommendation

arXiv: https://arxiv.org/abs/2411.06826 · HTML: https://mlsanity.com/paper/2411.06826/

## 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 7 of its 16 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://mlsanity.com/paper/2302.01115/) (2023)
- [Adaptive Domain Interest Network for Multi-domain Recommendation](https://mlsanity.com/paper/2206.09672/) (2022)
- [POSO: Personalized Cold Start Modules for Large-scale Recommender Systems](https://mlsanity.com/paper/2108.04690/) (2021)
- [AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2211.12105/) (2022)

## Abstract (arXiv)

Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typically employs all experts for each instance, leading to scalability issue and low-discriminability between domains and experts. Furthermore, the design of commonly used domain-specific networks exacerbates the scalability issues. To tackle the problems, We propose a novel method named CESAA consists of Conditional Expert Selection (CES) Module and Adaptive Expert Aggregation (AEA) Module to tackle these challenges. Specifically, CES first combines a sparse gating strategy with domain-shared experts. Then AEA utilizes mutual information loss to strengthen the correlations between experts and specific domains, and significantly improve the distinction between experts. As a result, only domain-shared experts and selected domain-specific experts are activated for each instance, striking a balance between computational efficiency and model performance. Experimental results on both public ranking and industrial retrieval datasets verify the effectiveness of our method in MDR tasks.