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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                    <jats:italic>n<\/jats:italic>\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\n                    <jats:italic>relation-aware hashing<\/jats:italic>\n                    that extends locality-sensitive hashing to encode hop-\n                    <jats:italic>n<\/jats:italic>\n                    metapath semantics and builds metapath-specific hash blocks as a scalable recall layer for candidate generation. Second, we introduce a\n                    <jats:italic>multimodal learning-to-hash<\/jats:italic>\n                    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.\n                  <\/jats:p>","DOI":"10.1007\/s10618-026-01202-1","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T11:58:42Z","timestamp":1774958322000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Relation-aware multimodal data hashing for scalable recommendation systems"],"prefix":"10.1007","volume":"40","author":[{"given":"Zehao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huizhi","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varun","family":"Ojha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"1202_CR1","doi-asserted-by":"publisher","unstructured":"Aggarwal CC (2016) An introduction to recommender systems. 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