An efficient learning scheme is proposed for hypergraph ranking based on multiple optimizations. This scheme dynamically optimizes the hypergraph structure through the incidence matrix and employs adaptive hyperedge weight estimation based on gradient descent method. By doing so, optimized hypergraph ranking vectors are derived, which recommend images of places of interest (POIs) worth visiting or tags to images associated to POIs. Experiments are conducted on a dataset of Greek POIs crawled from $Flickr$ and the NUS-WIDE-LITE dataset. The proposed method offers higher quality recommendations than state-of-the-art methods, employing either a static hypergraph structure, a hypergraph structure learning, or a hyperedge weight update with respect to precision.

提出了一种基于多重优化的超图排序的高效学习方案。该方案通过关联矩阵动态优化超图结构，并采用基于梯度下降法的自适应超边权重估计。通过这样做，可以导出优化的超图排名向量，推荐值得访问的兴趣点 (POI) 图像或为与 POI 相关联的图像添加标签。实验是在希腊 POI 的数据集上进行的，这些数据集是从$F升\mathrm{一世}C克r$和 NUS-WIDE-LITE 数据集。与最先进的方法相比，所提出的方法提供了更高质量的推荐，采用静态超图结构、超图结构学习或超边权重更新相对于精度。