The proposed HyperGC is a hybrid self-supervised HRL framework that unifies generative and contrastive learning through two core strategies: (S1) full hyperedge reconstruction via progressive selection with dynamic target cross-entropy, and (S2) multi-view contrastive evaluation without handcrafted negative samples.
D. Y. Kang, So-Bin Jung, Sang-Wook Kim· Proceedings of the 32nd ACM...· 0 citations
RaceMED is proposed, which incorporates properties (1) and (2) through a dual-branch encoder composed of race-specific and race-general branches that capture idiosyncratic features unique to each racial group and general patterns shared across all patients, and a predictor based on race-aware attention that restricts c...
Hojung Shin, Taeri Kim, Jebum Choi et al.· Proceedings of the 32nd ACM...· 0 citations
The goal of Cross-domain Recommender System (CDRS) is to recommend items in a target domain for users who have no target-domain interactions by leveraging their source-domain interaction histories. Most existing CDRSs transfer a user embedding from the source domain to the target domain and predict ratings via embeddin...
Jiwon Son, Y. Kwon, Sang-Wook Kim· Proceedings of the 32nd ACM...· 0 citations
Clinical evidence indicates that (1) disease prevalence differs across racial groups and (2) medication prescriptions for identical diagnoses and procedures may vary across racial groups. However, existing medication recommendation methods have not integrated these racial properties into the design of their encoder and...
Hojung Shin, Taeri Kim, Jebum Choi et al.· Proceedings of the 32nd ACM...· 0 citations
TAHB (Text-Attributed Hypergraph Benchmark) is presented, the first public benchmark integrating hypergraph structures and raw textual attributes, and shows that LLM-enhanced textual semantics improve hypergraph learning performance, while structural and textual information jointly provide the best setting for LLM-base...
D. Y. Kang, Junghyun Kim, Ju-hyun Jeon et al.· 0 citations
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