Ligang Dong

Yin Zhang, Can Xu, XianJun Wu et al.

Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities like last.fm or movielens. Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation. However, some solutions are directly inherited from GCN without justifications, which is difficult to alleviate the sparsity, ambiguity, and redundancy issues introduced by tags, thus adding to difficulties of training and degrading recommendation performance. In this work, we aim to simplify the design of GCN to make it more concise for TRS. We propose a novel tag-aware recommendation model named Light Folksonomy Graph Collaborative Filtering (LFGCF), which only includes the essential GCN components. Specifically, LFGCF first constructs Folksonomy Graphs from the records of user assigning tags and item getting tagged. Then we leverage the simple design of aggregation to learn the high-order representations on Folksonomy Graphs and use the weighted sum of the embeddings learned at several layers for information updating. We share tags embeddings to bridge the information gap between users and items. Besides, a regularization function named TransRT is proposed to better depict user preferences and item features. Extensive hyperparameters experiments and ablation studies on three real-world datasets show that LFGCF uses fewer parameters and significantly outperforms most baselines for the tag-aware top-N recommendations.

Zhebin Jin, Ligang Dong

Driver fatigue detection is of paramount importance for intelligent transportation systems due to its critical role in mitigating road traffic accidents. While physiological and vehicle dynamics-based methods offer accuracy, they are often intrusive, hardware-dependent, and lack robustness in real-world environments. Vision-based techniques provide a non-intrusive and scalable alternative, but still face challenges such as poor detection of small or occluded objects and limited multi-scale feature modeling. To address these issues, this paper proposes YOLO11-CR, a lightweight and efficient object detection model tailored for real-time fatigue detection. YOLO11-CR introduces two key modules: the Convolution-and-Attention Fusion Module (CAFM), which integrates local CNN features with global Transformer-based context to enhance feature expressiveness; and the Rectangular Calibration Module (RCM), which captures horizontal and vertical contextual information to improve spatial localization, particularly for profile faces and small objects like mobile phones. Experiments on the DSM dataset demonstrated that YOLO11-CR achieves a precision of 87.17%, recall of 83.86%, mAP@50 of 88.09%, and mAP@50-95 of 55.93%, outperforming baseline models significantly. Ablation studies further validate the effectiveness of the CAFM and RCM modules in improving both sensitivity and localization accuracy. These results demonstrate that YOLO11-CR offers a practical and high-performing solution for in-vehicle fatigue monitoring, with strong potential for real-world deployment and future enhancements involving temporal modeling, multi-modal data integration, and embedded optimization.