ERS: An Explainable Research Paper Recommendation System for User-Centric Discovery with LIME

In the era of growing academic research, the challenge of efficiently discovering relevant and valuable papers persists, exacerbated by the ever-growing volume of publications. Traditional recommendation systems, while offering assistance, often lack transparency, leaving users in the dark about the rationale behind suggestions. This research proposes an Explainable Research Paper Recommendation System (ERS) that leverages the Locally Interpretable Model-agnostic Explanations (LIME) algorithm. Our ERS personalizes recommendations by analyzing user profiles, research interests, and citation history. Uniquely, ERS offers transparent explanations for each recommendation, utilizing LIME to highlight the key factors influencing each suggested paper. This fosters user trust and empowers researchers with a deeper understanding of the recommendation process. Furthermore, the paper explores the potential of ERS to enhance academic collaboration, knowledge-sharing, and scalability, while contributing to the advancement of Explainable AI research. Ultimately, by delivering a positive user experience through relevant recommendations and clear explanations, such as systems can empower researchers to navigate the vast ocean of academic literature.