mlsanityData to 7 Oct 2026

HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation

arXiv 2511.06388 ↗First posted 9 Nov 2025cs.IR cs.AIcited by 0

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

Abstract (arXiv)

We propose HyMoERec, a novel sequential recommendation framework that addresses the limitations of uniform Position-wise Feed-Forward Networks in existing models. Current approaches treat all user interactions and items equally, overlooking the heterogeneity in user behavior patterns and diversity in item complexity. HyMoERec initially introduces a hybrid mixture-of-experts architecture that combines shared and specialized expert branches with an adaptive expert fusion mechanism for the sequential recommendation task. This design captures diverse reasoning for varied users and items while ensuring stable training. Experiments on MovieLens-1M and Beauty datasets demonstrate that HyMoERec consistently outperforms state-of-the-art baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MoE for recommender systems, one of 12 directions in Mixture-of-experts. Builds on 2 of its 3 references that are in the map. First posted on arXiv 9 Nov 2025.

Builds on

2 of 3 references.

References · 2

  1. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  2. 2017Neural Attentive Session-based Recommendationcited by 237

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