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Kaimin Wei

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2 papers

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AAAI Conference 2026 Conference Paper

DiMA: Distinguishing Resident and Tourist Preferences via Multi-Modal LLM Alignment for Out-of-Town Cross-Domain Recommendation

Fan Zhang
Jinpeng Chen
Tao Wang
Huan Li
Senzhang Wang
Feifei Kou
Ye Ji
Kaimin Wei

Out-of-Town (OOT) recommendation aims to provide personalized suggestions for users in unfamiliar cities. However, OOT recommendation faces two fundamental challenges: the difficulty of reasoning across modalities, as preference signals in disparate formats such as images and text are hard to compare; and the preference deviation problem, since a user's resident and tourist preferences often diverge, rendering simple preference transfer ineffective. To address these challenges, we propose Distinguishing Resident and Tourist Preferences via Multi-Modal LLM Alignment for Out-of-Town Cross-Domain Recommendation (DiMA), a framework for re-ranking Points of Interest (POIs). To tackle the multimodal challenge, DiMA first leverages Multimodal Large Language Models and Large Language Models (LLMs) to transform heterogeneous POI data into unified semantic tags, enabling both cross-modal reasoning and efficient downstream processing. To address preference deviation, a ``teacher'' LLM executes a custom Chain-of-Thought (CoT) process to disentangle resident and tourist preferences from multi-city histories for re-ranking. Finally, a lightweight student model learns this CoT reasoning via Supervised Fine-Tuning and is then refined with Direct Preference Optimization to align with true user choices, with the potential to surpass the teacher. Extensive experiments on a real-world dataset demonstrate that DiMA significantly enhances the performance of baseline models in the OOT recommendation re-ranking task.

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JBHI Journal 2026 Journal Article

Medical Image Privacy in Federated Learning: Segmentation-Reorganization and Sparsified Gradient Matching Attacks

Kaimin Wei
Jin Qian
Chengkun Jia
Jinpeng Chen
Jilian Zhang
Yongdong Wu
Jinyu Zhu
Yuhan Guo

In modern medicine, the widespread use of medical imaging has greatly improved diagnostic and treatment efficiency. However, these images contain sensitive personal information, and any leakage could seriously compromise patient privacy, leading to ethical and legal issues. Federated learning (FL), an emerging privacy-preserving technique, transmits gradients rather than raw data for model training. Yet, recent studies reveal that gradient inversion attacks can exploit this information to reconstruct private data, posing a significant threat to FL. Current attacks remain limited in image resolution, similarity, and batch processing, and thus do not yet pose a significant risk to FL. To address this, we propose a novel gradient inversion attack based on sparsified gradient matching and segmentation reorganization (SR) to reconstruct high-resolution, high-similarity medical images in batch mode. Specifically, an $L_{1}$ loss function optimises the gradient sparsification process, while the SR strategy enhances image resolution. An adaptive learning rate adjustment mechanism is also employed to improve optimisation stability and avoid local optima. Experimental results demonstrate that our method significantly outperforms state-of-the-art approaches in both visual quality and quantitative metrics, achieving up to a 146% improvement in similarity.

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