The increasing accessibility of mobile technologies and devices, such as smartphones and tablet PCs, has made mobile learning (m-learning) a critical feature of modern didactics. Mobile learning is among the many computerized activities that can be performed using mobile devices. As the volume of accessible important information on university websites continues to increase, students may face difficulties in accessing important information from a large dataset. This study introduces an algorithmic framework for data reduction that is built on optimized-memory map–reduce algorithm for mobile learning. The goal of this method is to generate meaningful recommendations to a collection of students in the easiest and fastest way by using a recommender system. Through an experiment, the proposed method has demonstrated significant improvements in data size reduction up to 77 %. Such improvements are greater than those that are achieved using alternate methods.