基于阶段衍变双向自均衡的个性化学习资源推荐方法

doi:10.16451/j.cnki.issn1003-6059.201810006

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Abstract

The existing personalized learning resource recommendation methods have problems of single model and low speed and matching degree. Therefore a personalized learning resource recommendation method based on stage evolution bidirectional self-balancing(EBPLRM-M) is proposed. Firstly, a personalized learning resource recommendation model is built based on stage evolution bidirectional self-balancing. The parameterized representations of resource recommendation features and fitness functions are improved. Then, a fuzzy adaptive binary particle swarm optimization algorithm based on evolutionary state determination is adopted to optimize the model. Finally, simulation results show that EBPLRM-M has higher matching degree and recommended speed compared with the recommendation methods adopting the classical algorithms.

Received: 25 June 2018

ZTFLH:

TP 311

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Supported by National Social Science Foundation of China(No.16BTQ084)

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	Articles by authors
	LI Haojun
	ZHANG Zheng
	ZHANG Pengwei

Cite this article:

LI Haojun,ZHANG Zheng,ZHANG Pengwei. Personalized Learning Resource Recommendation Method Based on Stage Evolution Bidirectional Self-balancing[J]. , 2018, 31(10): 921-932.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201810006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I10/921

[1] DE-MARCOS L, MARTINEZ J J, GUTI RREZ J A. Swarm Intelligence in E-Learning: A Learning Object Sequencing Agent Based on Competencies // Proc of the Conference on Genetic and Evolutionary Computation. New York, USA: ACM, 2008: 17-24.
[2] KURILOVAS E, ZILINSKIENE I, DAGIENE V. Recommending Suitable Learning Scenarios According to Learners′ Preferences: An Improved Swarm Based Approach. Computers in Human Behavior, 2014, 30: 550-557.
[3] 刘慧婷,杨良全,凌超,等.社交网络中融合社交关系和语义信息的推荐算法.模式识别与人工智能, 2018, 31(3): 236-244.
(LIU H T, YANG L Q, LING C, et al. Recommendation Algorithm with Social Relations and Content Information in Social Networks. Pattern Recognition and Artificial Intelligence, 2018, 31(3): 236-244.)
[4] 张宜浩,刘小洋,刘万平,等.基于图的混合推荐算法.模式识别与人工智能, 2017, 30(4): 335-342.
(ZHANG Y H, LIU X Y, LIU W P, et al. Hybrid Recommender Algorithm Based on Graph. Pattern Recognition and Artificial Inte-lligence, 2017, 30(4): 335-342.)
[5] QIAN F L, YUE R X, ZHAO S, et al. Robust Recommendation Algorithm Using an Iterative Group-Based Reputation // Proc of the 3rd IEEE International Conference on Cloud Computing and Big Data Analysis. Washington, USA: IEEE, 2018: 62-70.
[6] DE-MARCOS L, GARCIA-CABOT A, GARCIA-LOPEZ E, et al. Parliamentary Optimization to Build Personalized Learning Paths: Case Study in Web Engineering Curriculum. International Journal of Engineering Education, 2015, 31(4): 1092-1105.
[7] 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.
[8] 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 International Conference on Natural Computation. Washington, USA: IEEE, 2010: 2677-2681.
[9] 谢振平,金晨,刘渊.基于建构主义学习理论的个性化知识推荐模型.计算机研究与发展, 2018, 55(1): 125-138.
(XIE Z P, JIN C, LIU Y. Personalized Knowledge Recommendation Model Based on Constructivist Learning Theory. Journal of Computer Research and Development, 2018, 55(1): 125-138.)
[10] CHU C P, CHANG Y C, TSAI C C. PC2PSO: Personalized e-Course Composition Based on Particle Swarm Optimization. App-lied Intelligence, 2011, 34(1): 141-154.
[11] WAN S S, NIU Z D. A Learner Oriented Learning Recommendation Approach Based on Mixed Concept Mapping and Immune Algorithm. Knowledge-Based Systems, 2016, 103: 28-40.
[12] BIRJALI M, BENI-HSSANE A, ERRITALI M. A Novel Adaptive E-Learning Model Based on Big Data by Using Competence-Based Knowledge and Social Learner Activities. Applied Soft Computing, 2018, 69: 14-32.
[13] CHRISTUDAS B C L, KIRUBAKARAN E, THANGAIAH P R J. An Evolutionary Approach for Personalization of Content Delivery in e-Learning Systems Based on Learner Behavior Forcing Compatibility of Learning Materials. Telematics and Informatics, 2018, 35(3): 520-533.
[14] 刘淇,陈恩红,朱天宇,等.面向在线智慧学习的教育数据挖掘技术研究.模式识别与人工智能, 2018, 31(1): 77-90.
(LIU Q, CHEN E H, ZHU T Y, et al. Research on Educational Data Mining for Online Intelligent Learning. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 77-90.)
[15] ZHANG Y M, LEI T P, WANG Y, et al. A Service Recommendation Algorithm Based on Modeling of Dynamic and Diverse Demands. International Journal of Web Services Research, 2018, 15(1): 47-70.
[16] ZHU H P, TIAN F, WU K, et al. A Multi-constraint Learning Path Recommendation Algorithm Based on Knowledge Map. Knowledge-Based Systems, 2018, 143: 102-114
[17] ZHOU Y W, HUANG C Q, HU Q T, et al. Personalized Learning Full-Path Recommendation Model Based on LSTM Neural Networks. Information Sciences, 2018, 444: 135-152.
[18] 李学俊,徐佳,朱二周,等.任务调度算法中新的自适应惯性权重计算方法.计算机研究与发展, 2016, 53(9): 1990-1999.
(LI X J, XU J, ZHU E Z, et al. A Novel Computation Method for Adaptive Inertia Weight of Task Scheduling Algorithm. Journal of Computer Research and Development, 2016, 53(9): 1990-1999.)
[19] 张聚伟,王宇,杨挺.基于模糊粒子群算法的有向传感器网络路径覆盖策略.模式识别与人工智能, 2017, 30(2): 183-192.
(ZHANG J W, WANG Y, YANG T. Path Coverage Scheme Based on Fuzzy Particle Swarm Optimization Algorithm for Directional Sensor Networks. Pattern Recognition and Artificial Intelligence, 2017, 30(2): 183-192.)
[20] 高尚,杨静宇.混沌粒子群优化算法研究.模式识别与人工智能, 2006, 19(2): 266-270.
(GAO S, YANG J Y. Research on Chaos Particle Swarm Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 2006, 19(2): 266-270.)
[21] MENG X L, JIA L M. A New Kind of PSO-Convergent Fuzzy Particle Swarm Optimization and Performance Analysis // Proc of the 4th International Conference on Networked Computing and Advan-
ced Information Management. Washington, USA: IEEE, 2008: 102-107.
[22] DHEEBAN S G, DEEPAK V, DHAMODHARAN 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.
[23] MIRJALILI S, LEWIS A. S-Shaped versus V-Shaped Transfer Functions for Binary Particle Swarm Optimization. Swarm and Evolutionary Computation, 2013, 9: 1-14.