Optimizing ELARS Algorithms Using NVIDIA CUDA Heterogeneous Parallel Programming Platform (CROSBI ID 615545)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Miletić, Vedran ; Holenko Dlab, Martina ; Hoić- Božić, Nataša ;
engleski
Optimizing ELARS Algorithms Using NVIDIA CUDA Heterogeneous Parallel Programming Platform
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
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Podaci o prilogu
135-144.
2015.
objavljeno
Podaci o matičnoj publikaciji
ICT Innovations 2014, Advances in Intelligent Systems and Computing
Bogdanova, Ana Madevska ; Gjorgjevikj, Dejan
Berlin : Heidelberg: Springer
978-3-319-09878-4
Podaci o skupu
Nepoznat skup
predavanje
29.02.1904-29.02.2096