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.
[1] CHU C P, CHANG Y C, TSAI C C. PC2PSO: Personalized e-Course Composition Based on Particle Swarm Optimization. Applied Intelligence, 2011, 34(1): 141-154. [2] DHEEBAN S G, DEEPAK V, DHAMODHARN L, et al. Improved Personalized e-Course Composition Approach Using Modified Particle Swarm Optimization with Inertia-Coefficient. International Journal of Computer Applications, 2011, 1(6): 102-107. [3] SARATH C A P, DHEEBAN S G, DEEPAK V, et al. Personalized e-Course Composition Approach Using Digital Pheromones in Improved Particle Swarm Optimization // Proc of the 6th International Conference on Natural Computation. Washington, USA: IEEE, 2010: 2677-2681. [4] DE-MARCOS L, MARTINEZ J, GUTIÉRREZ J. Particle Swarms for Competency-Based Curriculum Sequencing // Proc of the World Summit on Knowledge Society. Berlin, Germany: Springer-Verlag, 2008: 243-252. [5] HUANG T C, HUANG Y M, CHENG S C. Automatic and Interactive e-Learning Auxiliary Material Generation Utilizing Particle Swarm Optimization. Expert Systems with Applications, 2008, 35(4): 2113-2122. [6] WANG T I, TSAI K H. Interactive and Dynamic Review Course Composition System Utilizing Contextual Semantic Expansion and Discrete Particle Swarm Optimization. Expert Systems with Applications, 2009, 36(6): 9663-9673. [7] 李浩君,张 广,王万良,等.基于多维特征差异的个性化学习资源推荐方法.系统工程理论与实践, 2017, 37(11): 2995-3005. (LI H J, ZHANG G, WANG W L, et al. The Method of Personalized Learning Materials Recommendation Based on Multidimensional Feature Difference. Systems Engineering-Theory and Practice, 2017, 37(11): 2995-3005.) [8] AMOLD J,程晓堂.情感与语言学习.北京:外语教学与研究出版社, 2000. (AMOLD J, CHENG X T. Emotion and Language Learning. Beijing, China: Foreign Language Teaching and Research Press, 2000) [9] SAXENA N, MISHRA K K. Improved Multi-objective Particle Swarm Optimization Algorithm for Optimizing Watermark Strength in Color Image Watermarking. Applied Intelligence, 2017, 47(2): 362-381. [10] 汤小月,余 伟,李石君.D3MOPSO:一种基于用户偏好的元搜索排序聚合演化方法.计算机研究与发展, 2017, 54(8): 1665-1681. (TANG X Y, YU W, LI S J. D3MOPSO: An Evolutionary Method for Metasearch Rank Aggregation Based on User Preferences. Journal of Computer Research and Development, 2017, 54(8): 1665-1681.) [11] 胡成玉,姚 宏,颜雪松.基于多粒子群协同的动态多目标优化算法及应用.计算机研究与发展, 2013, 50(6): 1313-1323. (HU C Y, YAO H, YAN X S. Multiple Particle Swarms Coevolutionary Algorithm for Dynamic Multi-objective Optimization Problems and Its Application. Journal of Computer Research and Deve-lopment, 2013, 50(6): 1313-1323.) [12] GAO Y, PENG L X, LI F F, et al. A Multi-objective PSO with Pareto Archive for Personalized e-Course Composition in Moodle Learning System // Proc of the 8th International Symposium on Computational Intelligence and Design. Washington, USA: IEEE, 2015: 21-24. [13] YANG Y J, WU C. An Attribute-Based Ant Colony System for Adaptive Learning Object Recommendation. Expert Systems with Applications, 2009, 36(2): 3034-3047. [14] 程 岩.在线学习中基于群体智能的学习路径推荐方法.系统管理学报, 2011, 20(2): 232-237. (CHENG Y. A Method of Swarm Intelligence-Based Learning Path Recommendation for Online Learning. Journal of Systems and Ma-nagement, 2011, 20(2): 232-237.) [15] ZHANG Q F, LI H. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731. [16] PAN A Q, WANG L, GUO W A, et al. A Diversity Enhanced Multiobjective Particle Swarm Optimization. Information Sciences, 2018, 436/437: 441-465. [17] LI K, FIALHO A, KWONG S, et al. Adaptive Operator Selection with Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 2014, 18(1): 114-130. [18] COELLO C A C, PULIDO G T, LECHUGA M S. Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279. [19] SCHÜTZE O, ESQUIVEL X, LARA A, et al. Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(4): 504-522. [20] SCHOTT J R. Fault Tolerant Design Using Single and Multi-criteria Genetic Algorithm Optimization. Master Dissertation. Boston, USA: Massachusetts Institute of Technology, 1995.
2019, Vol. 32 
