Optimizing Mobile App Recommendations Using Crowdsourced Educational Data

Abstract

In the rapidly evolving digital era, personalized recommendations play a crucial role in enhancing the educational experience of students. With the increasing use of mobile devices, it has become easier to collect app usage data, which can be leveraged to provide tailored educational app suggestions. This study focuses on recommending suitable applications for university students, specifically targeting Undergraduate (UG), Postgraduate (PG), and Graduate levels, based on their app usage patterns. 

The dataset is preprocessed using Natural Language Processing (NLP) techniques, including text cleaning, tokenization, and feature extraction, to capture relevant attributes from app descriptions and student interaction data. A Random Forest Classifier is employed as the core model due to its robustness, ability to handle highdimensional data, and strong performance in classification tasks. The proposed system accurately categorizes students’ preferences and recommends apps aligned with their academic needs. Experimental results highlight the efficiency of Random Forest in producing reliable, scalable, and interpretable recommendations compared to traditional methods. This approach ensures better personalization, reduces data sparsity issues, and enhances overall user satisfaction in educational recommendation systems.