Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis

These authors contributed equally to this work. Abstract Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various disciplines, anytime and anywhere. To enhance learner engagement, it is crucial to recommend courses that align with their preferences and needs. Course Recommender Systems (RSs) can play an important role in this by modeling learners' preferences based on their previous interactions within the MOOC platform. Time-to-dropout and time-to-completion in MOOCs, like other time-to-event prediction tasks, can be effectively modeled using survival analysis (SA) methods. In this study, we apply SA methods to improve collaborative filtering recommendation performance by considering time-to-event in the context of MOOCs. The findings underscore the potential of integrating SA methods with RSs to enhance personalization in MOOCs. Keywords: recommendation systems, survival analysis, massive open online course, personalized learning, dropout 1 Introduction Massive Open Online Courses (MOOCs) platforms offer a diverse range of online courses to learners around the globe, promoting equitable education by breaking down barriers related to geography and time.