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<p>In service-oriented computing environments, intelligent service ecosystems are characterized by dynamic service composition, evolving user preferences, and heterogeneous QoS constraints. Accurate modeling of user–service interaction sequences is therefore essential for adaptive and QoS-aware recommendations. However, existing sequential recommendation models typically rely on a single architectural paradigm, limiting their ability to capture multi-granularity behavioral dynamics. To address these challenges, we propose MageRec: a Parallel Mamba–Attention Gated Network for Sequential Recommendation. MageRec adopts a parallel architecture in which the Mamba and attention branches jointly model global contextual dependencies in user interaction sequences. Meanwhile, a local convolution module is introduced to extract local behavior patterns within short interaction neighborhoods. A context bias network further enables adaptive fusion of these heterogeneous signals under evolving service conditions. Extensive experiments on multiple public datasets demonstrate that MageRec consistently outperforms existing baselines. Our code is made publicly available on <a href="https://github.com/cccsama/MageRec">https://github.com/cccsama/MageRec</a>.</p>

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MageRec: a parallel mamba–attention gated network for sequential recommendation

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Xi Wang,
  • HongBin Xia

摘要

In service-oriented computing environments, intelligent service ecosystems are characterized by dynamic service composition, evolving user preferences, and heterogeneous QoS constraints. Accurate modeling of user–service interaction sequences is therefore essential for adaptive and QoS-aware recommendations. However, existing sequential recommendation models typically rely on a single architectural paradigm, limiting their ability to capture multi-granularity behavioral dynamics. To address these challenges, we propose MageRec: a Parallel Mamba–Attention Gated Network for Sequential Recommendation. MageRec adopts a parallel architecture in which the Mamba and attention branches jointly model global contextual dependencies in user interaction sequences. Meanwhile, a local convolution module is introduced to extract local behavior patterns within short interaction neighborhoods. A context bias network further enables adaptive fusion of these heterogeneous signals under evolving service conditions. Extensive experiments on multiple public datasets demonstrate that MageRec consistently outperforms existing baselines. Our code is made publicly available on https://github.com/cccsama/MageRec.