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<p>Recommender systems based on graph neural networks have demonstrated strong capability in modeling complex user–item interactions; however, their performance is often hindered by data sparsity, cold-start scenarios, and negative interference among heterogeneous learning objectives. In this paper, we propose a multi-task heterogeneous graph representation framework that jointly addresses these challenges at both the data and model levels. At the data level, we enrich the user–item interaction graph from <i>MovieLens</i> by integrating complementary semantic information from the <i>IMDb</i> knowledge graph, resulting in a multi-type heterogeneous graph that captures both behavioral interactions and item-level semantics. At the model level, we design a dual-branch architecture composed of two independently pre-trained, task-oriented graph encoders. The first branch employs a multi-task Graph Convolutional Network (GCN), with a primary focus on recommendation, to effectively model interaction intensity and structural patterns. The second branch utilizes a multi-task Heterogeneous Graph Transformer (HGT) that emphasizes node classification by explicitly capturing diverse node and relation types, thereby learning semantically coherent and discriminative representations. Each branch is optimized using task-weighted objectives to reduce negative task interference and preserve task-specific inductive biases. To integrate the complementary representations learned by the two branches, we introduce a bidirectional cross-attention mechanism that enables adaptive information exchange between interaction-driven and semantic-driven latent spaces. The fused representations are subsequently fine-tuned end-to-end to support recommendation and classification tasks jointly. Extensive experiments demonstrate that the proposed framework achieves more stable learning and superior performance compared to conventional GNN-based recommender models, highlighting the effectiveness of combining heterogeneous graph enrichment, task-oriented multi-task learning, and cross-attention-based feature fusion.</p>

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A multi-task heterogeneous graph learning with cross-attention fusion for robust recommendation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月18日

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  • Amin Nazari,
  • Muharram Mansoorizadeh,
  • Hassan Khotanlou

摘要

Recommender systems based on graph neural networks have demonstrated strong capability in modeling complex user–item interactions; however, their performance is often hindered by data sparsity, cold-start scenarios, and negative interference among heterogeneous learning objectives. In this paper, we propose a multi-task heterogeneous graph representation framework that jointly addresses these challenges at both the data and model levels. At the data level, we enrich the user–item interaction graph from MovieLens by integrating complementary semantic information from the IMDb knowledge graph, resulting in a multi-type heterogeneous graph that captures both behavioral interactions and item-level semantics. At the model level, we design a dual-branch architecture composed of two independently pre-trained, task-oriented graph encoders. The first branch employs a multi-task Graph Convolutional Network (GCN), with a primary focus on recommendation, to effectively model interaction intensity and structural patterns. The second branch utilizes a multi-task Heterogeneous Graph Transformer (HGT) that emphasizes node classification by explicitly capturing diverse node and relation types, thereby learning semantically coherent and discriminative representations. Each branch is optimized using task-weighted objectives to reduce negative task interference and preserve task-specific inductive biases. To integrate the complementary representations learned by the two branches, we introduce a bidirectional cross-attention mechanism that enables adaptive information exchange between interaction-driven and semantic-driven latent spaces. The fused representations are subsequently fine-tuned end-to-end to support recommendation and classification tasks jointly. Extensive experiments demonstrate that the proposed framework achieves more stable learning and superior performance compared to conventional GNN-based recommender models, highlighting the effectiveness of combining heterogeneous graph enrichment, task-oriented multi-task learning, and cross-attention-based feature fusion.