In recent years, Large Language Models (LLMs) have gained increasing attention in personalized recommendation systems due to their strong capabilities to understand contextual and semantic information. However, most existing LLM-based recommendation methods primarily rely on textual features, often neglecting the unique structural information embedded in user and item IDs, which are critical for capturing personalized preferences. To bridge this gap, we propose a novel method called CL 3M - Rec, which enhances the semantic representation of user and item IDs through Contrastive Learning (CL) and optimizes LLM recommendation performance via prompt construction. By effectively integrating structured ID information with textual semantics, our approach enables the LLMs to better model individual user interests and improve personalized prediction. Experiments on four public datasets demonstrate that CL3M-Rec consistently outperforms existing LLM-based and CL-based recommendation approaches, achieving superior predictive accuracy and robust generalization across diverse recommendation scenarios. The code of our method is available at https://github.com/Lewis44zhou/CL-3M-Rec.

Traffic prediction is a complex task. In the traditional forecasting methodologies, there are limitations regarding generalization, as specifically observed in the pretraining approaches. This is particularly evident in models involving temporal and spatial attributes, where finetuning often encounters numerous challenges. With the emergence of graph prompt learning, a new finetuning paradigm has gained increasing application. In the context of the traffic prediction methods involving spatio-temporal attributes, we endeavor to enhance its performance in the downstream tasks through graph pretraining and prompt-based finetuning. In particular, we propose a traffic prediction model based on graph prompt learning. This model deeply mines the hierarchical information of traffic data and its temporal attributes, by employing the method of graph pretraining and prompt learning to enhance the model’s generalizability and accuracy. Extensive experiments conducted on two real-world traffic prediction datasets confirm the effectiveness of the proposed method.

In this study, we present an alternating controlled functional electrical stimulation (FES) strategy for rehabilitation of lower extremity motor function of hemiplegia after stroke. The muscle activity onset time, determined by using sample entropy (SampEn) analysis of an electromyographic (EMG) signal, is used as a trigger for FES to manage stimulations. The EMG-bridge (EMGB) type FES is a novel motor functional rehabilitation idea that it exploits sEMG signal from a healthy limb to regulate the stimulus parameters of stimulations applied to the paralyzed limb, so as to achieve synchronous movement of bilateral or different limbs. The alternating controlled FES strategy was realized on the basis of combing muscle activity onset time with EMGB-type FES system. Using this FES control strategy, experiments on a healthy subject have been carried out successfully to realize alternating stimulation to plantar flexor (PF) and dorsiflexor (DF) muscles of lower limb in sitting position.