IMPROVING APP RECOMMENDATIONS THROUGH CROWDSOURCED LEARNING DATA AND AI TECHNIQUES

Abstract

The rapid growth of mobile applications has created an overwhelming ecosystem where users often struggle to discover relevant and high-quality apps tailored to their needs. Traditional recommendation systems rely primarily on user behavior, ratings, and download statistics, which may not fully capture the educational value or contextual relevance of applications. In recent years, crowdsourced educational data—such as user reviews, feedback, usage patterns, and domain-specific annotations—has emerged as a valuable resource for enhancing recommendation systems. This research proposes a novel framework that integrates machine learning and deep learning techniques to improve mobile app recommendations by leveraging crowdsourced educational data. The proposed system combines collaborative filtering, content-based filtering, and deep neural networks to analyze user preferences and app characteristics. Natural Language Processing (NLP) is applied to extract meaningful insights from user reviews and feedback, enabling the identification of educational relevance, usability, and quality. Additionally, the model incorporates sentiment analysis and topic modeling to understand user opinions and preferences. Deep learning architectures, such as neural collaborative filtering and recurrent neural networks, are utilized to capture complex patterns in user interactions and textual data. The integration of crowdsourced data enhances the diversity and personalization of recommendations, providing users with more accurate and context-aware suggestions. Experimental results demonstrate that the proposed approach improves recommendation accuracy, precision, and user satisfaction compared to traditional methods. Furthermore, the system addresses challenges such as data sparsity and cold-start problems by utilizing rich contextual information from crowdsourced data. The framework is scalable and adaptable to various application domains, including educational apps, productivity tools, and entertainment platforms. Overall, this research highlights the potential of combining machine learning, deep learning, and crowdsourced data to develop intelligent recommendation systems that enhance user experience and support informed decision-making in mobile app ecosystems.