Recent advances in large language models (LLMs) have enabled promising progress in diagnosis prediction from electronic health records (EHRs). However, existing LLM-based approaches tend to overfit to historically observed diagnoses, often overlooking novel yet clinically important conditions that are critical for early intervention. To address this, we propose EviCare, an in-context reasoning framework that integrates deep model guidance into LLM-based diagnosis prediction. Rather than prompting LLMs directly with raw EHR inputs, EviCare performs (1) deep model inference for candidate selection, (2) evidential prioritization for set-based EHRs, and (3) relational evidence construction for novel diagnosis prediction. These signals are then composed into an adaptive in-context prompt to guide LLM reasoning in an accurate and interpretable manner. Extensive experiments on two real-world EHR benchmarks (MIMIC-III and MIMIC-IV) demonstrate that EviCare achieves significant performance gains, which consistently outperforms both LLM-only and deep model-only baselines by an average of 20.65% across precision and accuracy metrics. The improvements are particularly notable in challenging novel diagnosis prediction, yielding average improvements of 30.97%.

Accurate user profiles are crucial for personalized recommendation systems to mitigate information overload on large-scale online platforms. While recent advances in large language models have enhanced semantic understanding for profile construction through textual artifacts, existing methods often neglect the higher-order structural patterns inherent in user-item interaction graphs-a key limitation for achieving accurate and diverse recommendations. In this paper, we propose SSPRec, a Higher-order Structure and Semantics-enhanced User Profiling for Recommendation. Specifically, we first introduce a multi-hop proximity matrix over item-item transitions, followed by low-rank approximation and clustering to group users based on behavioral similarity. Group-level user profiles are then distilled via representative keywords extracted from co-interacted items, and collaborative embeddings are concurrently learned from the interaction graph. To integrate collaborative signals with language-based profiles, we introduce a cross-view contrastive objective that encourages coherence between structural and semantic representations. Final recommendations are made using a fused user-item similarity score. Extensive experiments on four real-world datasets show that SSPRec not only outperforms baselines in accuracy (with 46.35% improvements), but also remains diverse and robust, even under incomplete interactions.

In the dynamic environment of multimedia-sharing platforms like X (formerly known as Twitter) and TikTok, multimedia recommendation systems have been widely used to help users discover items of interest. However, traditional approaches often fall short, when the item modalities are incomplete, a common issue in real-world scenarios. To this end, we introduce the unified heterogeneous Hypergraph construction for the Incomplete multimedia REcommendation ( HIRE ), a novel framework designed to jointly learn a heterogeneous hypergraph and perform accurate recommendations under incomplete scenarios. HIRE first initializes the unified heterogeneous hypergraph for modality completion and employs self-supervised learning aligned with the contrastive text-centered view for multimedia recommendation. Such integration effectively handles the challenges posed by incomplete modalities, leading to improved recommendation accuracy. Furthermore, we find that the hypergraph directly learned from the HIRE is a dense structure which can be inaccurate and coarse. Therefore, we devise the HIRE framework with Sparse constraint named HIRES , which uniquely integrates optimal transport and a \(\ell_{2,1}\) -norm to refine the hypergraph structure. Our extensive experiments across various datasets demonstrate the superiority of HIRES in addressing incomplete modalities, establishing it as a powerful tool for personalized multimedia recommendations.