Efficiently processing large-scale graphs for information retrieval tasks presents a formidable hurdle, demanding innovative solutions for enhancing user experiences. This paper introduces a framework that merges attention-based graph summarization with state-of-the-art graph sampling methods tailored explicitly for large-scale graph processing and information retrieval applications, all aimed at enriching user experiences. Our approach distinguishes itself through its adeptness in efficiently handling vast graph datasets, leveraging robust sampling techniques and attention mechanisms to enhance feature extraction. Central to our methodology is the utilization of graph summarization techniques, which focus on distilling pertinent information, thereby enhancing both the accuracy and computational efficiency of information retrieval and recommendation tasks. Through practical demonstrations, notably within academic databases, our framework showcases its effectiveness in real-world scenarios, offering a significant advancement in the realm of personal technology data management and information retrieval systems.