Research on recommendation methods using multimodal graph information presents a significant challenge within the realm of information services. Prior studies in this area have lacked precision in the purification and denoising of multimodal information and have insufficiently explored fusion methods. We introduce a multimodal graph recommendation approach leveraging cross-attention fusion. This model enhances and purifies multimodal information by embedding the IDs of items and their corresponding interactive users, thereby optimizing the utilization of such information. To facilitate better integration, we propose a cross-attention mechanism-based multimodal information fusion method, which effectively processes and merges related and differential information across modalities. Experimental results on three public datasets indicated that our model performed exceptionally well, demonstrating its efficacy in leveraging multimodal information.

This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy and storage capacity. In this paper, a mixed-integer linear programming (MILP) model for the integrated scheduling of imaging data transmission has been proposed. The MILP model was validated through numerical experiments based on simulation data from SuperView-1 series satellites. Additionally, some neighborhood mechanisms are designed based on the characteristics of the problem. Based on the neighborhood mechanisms, the rule-based large neighborhood search algorithm (RLNS) was designed, which constructs initial solutions through various scheduling rules and iteratively optimizes the solutions using multiple destroying and repairing operators. To address the shortcomings of the overly regular mechanism of the destruction and repair operator for large neighborhood search, we design a genetic algorithms (GA) for tuning the heuristic scheduling rules. The calculation results demonstrate the effectiveness of RLNS and GA, highlighting their advantages over CPLEX in solving large-scale problems.