Peer link: Personalized Collaborative Partner Recommendation through Natural Language Processing and Collaborative Filtering

In today's globalized landscape, collaborative partnerships are indispensable drivers of innovation and growth across diverse sectors. However, identifying suitable collaborative partners can be a challenging task for people, particularly in large educational institutions, online learning platforms, or in the workplace. Also, when seeking a collaborative partner for participation in an event, it is imperative to identify an individual who not only comprehends one's perspective but also aligns with their approach and is prepared to engage in effective collaboration. This study proposes a Collaborative Partner Recommendation System (CPRS) designed to facilitate the formation of effective study groups and teams to participate in events. The CPRS employs a multi-faceted approach, considering factors such as learning preferences, performance, and personal criteria to match compatible collaborative partners. This platform facilitates global communication among students, enabling them to collaborate on projects or engage in international hackathons regardless of their geographical locations. Additionally, the CPRS integrates Natural Language Processing (NLP) and K-Nearest Neighbors (KNN) algorithms to enhance partner recommendations. NLP techniques are utilized to extract key skill sets from resumes, enabling a comprehensive understanding of users' backgrounds, interests, and goals. Subsequently, KNN is employed to identify the nearest neighbours within the feature space, further refining recommendation accuracy. This integrated approach streamlines the process of identifying suitable collaborative partners, facilitating effective collaboration and maximizing the potential of partnerships.