Optimizing Mobile App Recommendations Using Crowdsourced Educational Data
DOI:
https://doi.org/10.63665/m3wyh713Keywords:
educational recommendation systems, mobile learning, crowdsourced data, natural language processing, Random Forest, TF-IDF, interpretable machine learning.Abstract
This paper presents a content-aware educational recommendation framework for mobile applications used by university students. The system addresses the discovery problem that emerges when learners must select among many similar apps for study planning, note sharing, research support, productivity, and collaboration. The proposed approach combines crowdsourced app metadata with student interaction traces and applies NLP-based preprocessing to transform text into structured features. A Random Forest classifier is trained to predict the most suitable academic cohort - Undergraduate, Postgraduate, or Graduate - and the resulting class probabilities are used to rank apps by relevance. The framework is designed to be lightweight, interpretable, and easy to deploy in a web or cloud environment. Compared with GRU-based sequence modeling and other classical baselines, the ensemble method offers stronger stability on sparse educational data, better resistance to overfitting, and easier interpretation through feature importance. The manuscript formalizes the project as a complete IEEE-style paper, including problem definition, methodology, architecture, implementation, evaluation, and future extensions.
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