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However, these models lack the capability to capture the inherent comparability among samples, where multiple samples exhibit both shared and distinct characteristics, and such comparable relationships are often beneficial for prediction. To this end, we propose SR-HyperFM, Sample Relationship aware Hypergraph Factorization Machines, which incorporate sample comparable relationships into feature interaction modeling, leveraging both shared features and critical differences among samples. Specifically, the sample relationship aware hypergraph construction module is introduced to fully capture the comparable relationships among samples and discover beneficial high-order feature interactions. In addition, the dual hypergraph message passing module explicitly models feature interactions by exploiting these inherent relationships. Extensive experiments on four real-world datasets demonstrate the superiority of SR-HyperFM. In addition, case studies are conducted to further justify the effectiveness of SR-HyperFM.<\/jats:p>","DOI":"10.1145\/3773082","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T14:47:22Z","timestamp":1761576442000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SR-HyperFM: Sample Relationship Aware Hypergraph Factorization Machines for Feature Interaction Modeling"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8625-7135","authenticated-orcid":false,"given":"Hongyu","family":"Shi","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Blockchain and Data Security, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1934-5992","authenticated-orcid":false,"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Blockchain and Data Security, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5632-7630","authenticated-orcid":false,"given":"Qian","family":"Chen","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Blockchain and Data Security, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3530257"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01477-6"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01566-6"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3233770"},{"issue":"1","key":"e_1_3_1_6_2","first-page":"3","article-title":"Empirical study of soft clustering technique for determining click through rate in online advertising. 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