Scalability is an important property of every large-scale recommender system. In order to ensure smooth user experience, recommendation algorithms should be optimized to work with large amounts of user data. This paper presents the optimization approach used in the development of the E-learning activities recommender system (ELARS). The recommendations for students and groups in ELARS include four different types of items: Web 2.0 tools, collaborators (colleague students), optional e-learning activities, and advice. Since implemented recommendation algorithms depend on prediction of students’ preferences, algorithm that computes predictions was offloaded to graphics processing unit using NVIDIA CUDA heterogeneous parallel programming platform. This offload increases performance significantly, especially with large number of students using the system.
e-learning recommender system ELARS algorithm optimization heterogeneous paralell programming NVIDIA CUDA