A study on a recommendation algorithm based on spectral clustering and GRU.

With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.

Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4.

Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection.