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<p>Recommendation systems often contain both rich relational structures and diverse multimodal information. The multiple relations among users, items, and auxiliary entities naturally form a heterogeneous information network. A central challenge in developing scalable recommendation systems in the era of big data is efficiently identifying similar users and items across hop-<i>n</i> relational paths in such networks. Hashing has been widely adopted for dimensionality and data size reduction; however, existing techniques are primarily designed for directly connected (i.e., hop-1) features and rarely exploit higher-order relational information. To address this limitation, we propose two methods. First, we develop <i>relation-aware hashing</i> that extends locality-sensitive hashing to encode hop-<i>n</i> metapath semantics and builds metapath-specific hash blocks as a scalable recall layer for candidate generation. Second, we introduce a <i>multimodal learning-to-hash</i> model that learns binary codes from fused text, image, and temporal features, and aligns Hamming-space neighbourhoods with metapath-guided user neighbourhood graphs. By jointly leveraging both relation-aware encoding and multimodal content, the proposed approaches enable efficient neighbourhood construction and recommendation in large-scale heterogeneous networks. Extensive experiments on three real-world datasets show that our framework achieves substantial efficiency gains while delivering competitive recommendation accuracy compared with baselines.</p>

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Relation-aware multimodal data hashing for scalable recommendation systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Zehao Liu,
  • Huizhi Liang,
  • Varun Ojha

摘要

Recommendation systems often contain both rich relational structures and diverse multimodal information. The multiple relations among users, items, and auxiliary entities naturally form a heterogeneous information network. A central challenge in developing scalable recommendation systems in the era of big data is efficiently identifying similar users and items across hop-n relational paths in such networks. Hashing has been widely adopted for dimensionality and data size reduction; however, existing techniques are primarily designed for directly connected (i.e., hop-1) features and rarely exploit higher-order relational information. To address this limitation, we propose two methods. First, we develop relation-aware hashing that extends locality-sensitive hashing to encode hop-n metapath semantics and builds metapath-specific hash blocks as a scalable recall layer for candidate generation. Second, we introduce a multimodal learning-to-hash model that learns binary codes from fused text, image, and temporal features, and aligns Hamming-space neighbourhoods with metapath-guided user neighbourhood graphs. By jointly leveraging both relation-aware encoding and multimodal content, the proposed approaches enable efficient neighbourhood construction and recommendation in large-scale heterogeneous networks. Extensive experiments on three real-world datasets show that our framework achieves substantial efficiency gains while delivering competitive recommendation accuracy compared with baselines.