mlsanityData to 7 Oct 2026

M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

arXiv 2404.18465 ↗First posted 29 Apr 2024cs.IR cs.AIcited by 32

On the map: Mixture-of-experts → MoE for recommender systems

Abstract (arXiv)

Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually faces multiple domains and tasks simultaneously, which cannot be well-addressed by current methods. To this end, we introduce M3oE, an adaptive Multi-domain Multi-task Mixture-of-Experts recommendation framework. M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives. We leverage three mixture-of-experts modules to learn common, domain-aspect, and task-aspect user preferences respectively to address the complex dependencies among multiple domains and tasks in a disentangled manner. Additionally, we design a two-level fusion mechanism for precise control over feature extraction and fusion across diverse domains and tasks. The framework's adaptability is further enhanced by applying AutoML technique, which allows dynamic structure optimization. To the best of the authors' knowledge, our M3oE is the first effort to solve multi-domain multi-task recommendation self-adaptively. Extensive experiments on two benchmark datasets against diverse baselines demonstrate M3oE's superior performance. The implementation code is available to ensure reproducibility.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Placed in MoE for recommender systems, one of 12 directions in Mixture-of-experts. Builds on 16 of its 36 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

16 of 36 references.

References · 16

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  2. 2021One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Predictioncited by 78
  3. 2019DDTCDR: Deep Dual Transfer Cross Domain Recommendationcited by 71
  4. 2023PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Informationcited by 60
  5. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  6. 2022MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Taskscited by 34
  7. 2022AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learningcited by 31
  8. 2021A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directionscited by 30
  9. 2023Multi-Task Deep Recommender Systems: A Surveycited by 29
  10. 2023Multi-Task Recommendations with Reinforcement Learningcited by 26
  11. 2021SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarioscited by 23
  12. 2023HAMUR: Hyper Adapter for Multi-Domain Recommendationcited by 21
4 more
  1. 2022Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modelingcited by 18
  2. 2024ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systemscited by 9
  3. 2022AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Predictioncited by 7
  4. 2023Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 21; 2026: 10.

32 papers.

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  1. 2025X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Modelcited by 285
  2. 2024FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Expertscited by 27
  3. 2025MoE-Loco: Mixture of Experts for Multitask Locomotioncited by 17
  4. 2025Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendationcited by 15
  5. 2025Mixture of Experts in Large Language Modelscited by 15
  6. 2025Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendationcited by 14
  7. 2025Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrievalcited by 11
  8. 2025Hierarchical LoRA MoE for Efficient CTR Model Scalingcited by 10
  9. 2025Training-free LLM Merging for Multi-task Learningcited by 5
  10. 2025Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuningcited by 4
  11. 2026Zenith: Scaling up Ranking Models for Billion-scale Livestreaming Recommendationcited by 3
  12. 2026Learning to Compose for Cross-domain Agentic Workflow Generationcited by 3
20 more
  1. 2025Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDscited by 2
  2. 2025Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRIcited by 2
  3. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  4. 2025A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphscited by 1
  5. 2025Multi-modal Adaptive Mixture of Experts for Cold-start Recommendationcited by 1
  6. 2025Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendationcited by 1
  7. 2025Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuningcited by 1
  8. 2025LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Trainingcited by 1
  9. 2026SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendationcited by 1
  10. 2026Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learningcited by 1
  11. 2026Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendationcited by 1
  12. 2025RouterKT: Mixture-of-Experts for Knowledge Tracingcited by 0
  13. 2025Empowering Denoising Sequential Recommendation with Large Language Model Embeddingscited by 0
  14. 2025No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Predictioncited by 0
  15. 2025Distillation-based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendationcited by 0
  16. 2026CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendationcited by 0
  17. 2026Unbiased Rectification for Sequential Recommender Systems Under Fake Orderscited by 0
  18. 2026Action-Aware Generative Sequence Modeling for Short Video Recommendationcited by 0
  19. 2026Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Expertscited by 0
  20. 2026Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Studycited by 0

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  1. 2026SMES: Towards Scalable Multi-Task Recommendation via Expert Sparsity
  2. 2024Towards Personalized Federated Multi-Scenario Multi-Task Recommendation
  3. 2025MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
  4. 2025HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation
  5. 2024Adaptive Conditional Expert Selection Network for Multi-domain Recommendation
  6. 2025A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
  7. 2025MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation
  8. 2024Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation