mlsanityData to 14 Oct 2026

A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation

arXiv 2508.19507 ↗First posted 27 Aug 2025cs.IR cs.AIcited by 0

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

Abstract (arXiv)

In e-commerce, where users face a vast array of possible item choices, recommender systems are vital for helping them discover suitable items they might otherwise overlook. While many recommender systems primarily rely on a user's purchase history, recent multi-behavior recommender systems incorporate various auxiliary user behaviors, such as item clicks and cart additions, to enhance recommendations. Despite their overall performance gains, their effectiveness varies considerably between visited items (i.e., those a user has interacted with through auxiliary behaviors) and unvisited items (i.e., those with which the user has had no such interactions). Specifically, our analysis reveals that (1) existing multi-behavior recommender systems exhibit a significant gap in recommendation quality between the two item types (visited and unvisited items) and (2) achieving strong performance on both types with a single model architecture remains challenging. To tackle these issues, we propose a novel multi-behavior recommender system, MEMBER. It employs a mixture-of-experts framework, with experts designed to recommend the two item types, respectively. Each expert is trained using a self-supervised method specialized for its design goal. In our comprehensive experiments, we show the effectiveness of MEMBER across both item types, achieving up to 65.46% performance gain over the best competitor in terms of Hit Ratio@20.

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 21 of its 42 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

21 of 42 references.

References · 21

  1. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  2. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  3. 2017Neural Collaborative Filteringcited by 1,111
  4. 2019Neural Graph Collaborative Filteringcited by 661
  5. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  6. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  7. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  8. 2021Graph Meta Network for Multi-Behavior Recommendationcited by 38
  9. 2022Contrastive Meta Learning with Behavior Multiplicity for Recommendationcited by 37
  10. 2018Facial Expression Analysis under Partial Occlusion: A Surveycited by 28
  11. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  12. 2021Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendationcited by 21
9 more
  1. 2023Contrastive Collaborative Filtering for Cold-Start Item Recommendationcited by 15
  2. 2022Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendationcited by 12
  3. 2023Multi-behavior Self-supervised Learning for Recommendationcited by 11
  4. 2023Multi-Behavior Recommendation with Cascading Graph Convolution Networkscited by 8
  5. 2023Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendationcited by 6
  6. 2024Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendationcited by 6
  7. 2021Achieving Human Parity on Visual Question Answeringcited by 5
  8. 2025Multi-Behavior Recommender Systems: A Surveycited by 4
  9. 2023MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendationcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2018Recommendation Through Mixtures of Heterogeneous Item Relationships
  2. 2021On component interactions in two-stage recommender systems
  3. 2024A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
  4. 2026From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
  5. 2025HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation
  6. 2024M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
  7. 2026MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
  8. 2025Revisiting scalable sequential recommendation with Multi-Embedding Approach and Mixture-of-Experts