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<p>Cross-domain recommendation offers a promising solution to alleviate data sparsity and cold-start issues by transferring knowledge from a source domain. However, existing methods often fail to integrate multi-view user preferences and are susceptible to transfer noise due to domain gaps. To address these limitations, we propose MAPTRec, a multi-view attention-enhanced preference transfer framework. It explicitly models feature heterogeneity and semantic misalignment across domains. Specifically, MAPTRec introduces a heterogeneous feature-aware graph convolution (HFGC) module to model high-order collaborative signals and semantic coupling in interaction graphs. A cross-view alignment module (CAM) then aligns multi-view features via dual cross-attention and dynamic gating mechanisms. Finally, the self-attentive user encoder (SAU) applies multi-head attention to capture internal dependencies within fused features. Experiments on Amazon and Douban datasets demonstrate that MAPTRec consistently outperforms strong baselines in terms of Recall@K and NDCG@K, particularly under sparse and cold-start settings.</p>

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MAPTRec: a multi-view attention framework for heterogeneous cross-domain recommendation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月6日

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  • Qinghua Liu,
  • Qingping Li

摘要

Cross-domain recommendation offers a promising solution to alleviate data sparsity and cold-start issues by transferring knowledge from a source domain. However, existing methods often fail to integrate multi-view user preferences and are susceptible to transfer noise due to domain gaps. To address these limitations, we propose MAPTRec, a multi-view attention-enhanced preference transfer framework. It explicitly models feature heterogeneity and semantic misalignment across domains. Specifically, MAPTRec introduces a heterogeneous feature-aware graph convolution (HFGC) module to model high-order collaborative signals and semantic coupling in interaction graphs. A cross-view alignment module (CAM) then aligns multi-view features via dual cross-attention and dynamic gating mechanisms. Finally, the self-attentive user encoder (SAU) applies multi-head attention to capture internal dependencies within fused features. Experiments on Amazon and Douban datasets demonstrate that MAPTRec consistently outperforms strong baselines in terms of Recall@K and NDCG@K, particularly under sparse and cold-start settings.