JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (6): 13-24.doi: 10.6040/j.issn.1671-9352.1.2025.021

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Shared-account recommendation with mixture-of-experts

YUE Houping1, WANG Xinhua2*, GUO Lei1*, LIU Peiyu1, XU Liancheng1   

  1. 1. School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan 250358, Shandong, China;
    2. Shandong Normal University Library, Jinan 250358, Shandong, China
  • Published:2026-06-04

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Abstract: This paper addresses the issues of behavior sequence mixing and temporal changes in dominant preferences in the shared account TV recommendation scenario by proposing a mixture-of-experts shared account recommendation(MoE-SAR)algorithm. This method decomposes behavior sequences using a mixture of experts network and adaptively fuses expert outputs through a dynamic gating mechanism to accurately identify individual user characteristics. Additionally, the MoE-SAR method introduces a contrastive learning strategy to minimize the distance between samples from the same source expert and maximize the mutual information between outputs from different experts, effectively enhancing the discriminability and stability of representations. Furthermore, this approach integrates Transformers for temporal modeling to accurately capture the personalized preferences of individual users within shared accounts. The experimental results indicate that on the E-domain of the HVIDEO dataset, MoE-SAR improves MRR@20 by 24.0% and Recall@20 by 10.8% over the second-best baseline. On the V-domain, MRR@20 improves by 8.0% and Recall@20 improves by 4.7%.

Key words: shared account, mixture-of-experts, recommendation system

CLC Number: 

  • TP391
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