Abstract Single-objective transformation method is commonly used in online learning resource recommendation. In the recommendation process, the consideration of learner preference is inadequate. Therefore, the accuracy of learning resource recommendation is affected. An online learning resource recommendation model, multi-objective resource recommendation model(MOSRAM), is proposed based on multi-objective optimization strategy. In this model, learner preference for the type of learning resources and the fitness of the difficulty level are regarded as the optimization objectives in the planning time. A multi-objective particle swarm optimization algorithm, neighborhood multi-objective particle swarm optimization(NEMOPSO), with the ability to benefit from neighbor mean and explore new regions is designed. An online learning resource recommendation method, neighborhood multi-objective particle swarm optimization-resource recommendation approach(NEMOPSO-RA), based on MOSRAM model is proposed. The comparison of recommendation methods with classical multi-objective optimization algorithms under different problem scales show that the accuracy and performance of online learning resource recommendation can be effectively improved by NEMOPSO-RA method.
Fund:Supported by National Social Science Foundation of China(No.16BTQ084)
About author:: LI Haojun(Corresponding author), Ph.D., associate professor. His research interests include intelligent computing and intelligent learning.YANG Lin, master student. Her research interests include intelligent computing and intelligent learning.ZHANG Pengwei, master student. His research interests include intelligent computing and intelligent learning.
LI Haojun,YANG Lin,ZHANG Pengwei. Method of Online Learning Resource Recommendation Based on Multi-objective Optimization Strategy[J]. , 2019, 32(4): 306-316.
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2019, Vol. 32 
