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 embedding matching with target-domain item embeddings, which can overlook fine-grained user--item preference signals expressed in reviews. To capture such fine-grained signals for each target-domain user--item pair, we propose øurs, a conditional-diffusion-based CDRS that generates a target-domain review embedding and then predicts the corresponding rating from the generated embedding. øurs~ improves review-embedding generation with two key ideas: (Idea 1) target-aware source attention to construct review guidance (\ie, a conditioning embedding); and (Idea 2) pretraining on target-domain review embeddings from target-only users to learn a broader target-domain review-embedding distribution. Extensive experiments on three cross-domain scenarios show that øurs~ consistently outperforms nine competitors, reducing MAE by up to 14.2%.
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 predictor architectures. To address this gap, we propose RaceMED, 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 cross-patient visit references to patients from the same racial group, preventing inappropriate medication transfer across racial groups. Extensive experiments demonstrate that RaceMED consistently outperforms ten state-of-the-art competitors, achieving up to 12.24% improvement in accuracy while reducing performance disparities across racial groups by up to 31.77%, thereby improving fairness. These findings demonstrate that explicitly modeling racial properties is essential for improving both accuracy and fairness, a dimension that has been largely underexplored in the medication recommendation domain.
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-based prediction.
D. Y. Kang, Junghyun Kim, Ju-hyun Jeon et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
