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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 105-117    DOI: 10.11925/infotech.2096-3467.2019.1092
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Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation
Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei(),Huang Tiankuan
School of Computers, Guangdong University of Technology, Guangzhou 510006, China
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Abstract  

[Objective] This paper proposes a personalized model based on learning situation, which recommends schemes for learners and addresses the information overload issues.[Methods] First, we constructed a PAD-CF collaborative filtering algorithm based on three factors related to learning situation. Then, we introduced the knowledge map and degrees centrality of knowledge points to retrieve the recommended points.[Results] Compared to Pearson-CF, Edurank, and CF-SPM, the proposed model improved the F value by 6.24%, 2.68%, and 1.98%, respectively. The growth rates were 3.87%, 2.39%, and 1.43%.[Limitations] We need to add more complicated learning factors to improve the accuracy of predicted knowledge points.[Conclusions] The proposed model is highly practical for real world cases.
Key words： Learning Situation Similarity      Collaborative Filtering      Personalized Learning      Recommendation Model      Knowledge Map      Degree Centrality     
Received: 30 September 2019      Published: 15 June 2020
ZTFLH:  TP311.1  
Corresponding Authors: Li Xiaomei     E-mail: lixm@gdut.edu.cn
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Qing Su
Sizhao Chen
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Xiaomei Li
Tiankuan Huang

Cite this article:

Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation. Data Analysis and Knowledge Discovery, 2020, 4(5): 105-117.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1092     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I5/105

算法模型 优势 不足
Pearson-CF[15] 是经典的协同过滤算法,结合学习者的共同知识点平均分,使得相似度的计算更具客观性。 由于忽略了体现学习者学习情况的各种因素,导致相似度计算结果准确度欠佳。
New-cosine[7] 引入权重方程,提升了学习成绩较好学习者的推荐权重,进而改进协同过滤算法。 学习者的学习情况各异,仅以成绩较好的学习者作为推荐标准,缺乏个性化,影响推荐效果。
TRCF-LS-KL[8] 结合学习者学习风格、知识水平及信任模式对协同过滤算法进行改进。 仅通过问卷调查手段确定学习风格相对片面;由学习者指定被信任人的信任模式具有较大主观性。
CF-SPM[9] 融合学习者的学习情况（学习对象得分）以及学习风格（学习某对象的时间、频率）改进协同过滤算法。 仅以学习时间和频率等个体差异较大的因素计算学习者的相似度时,存在较大偏差,客观性不足。
Edurank[10] 联合协同过滤和社会选择理论,结合学习者以及相似学习群体的学习情况和认知水平改进协同过滤算法。 缺乏对学习者自身学习情况和学习风格等方面信息的挖掘,与个性化学习情况的结合程度较低。
Introduction of Classical Recommendation Model
Knowledge Map of C Programming
Example of Personalized Learning Scheme
Framework of LS-PLRM
Scoring Matrix of Students
Incidence Matrix of Question and Knowledge Point
Question Quantity Associated with Knowledge Points in dataset_one
Normalized Scoring Matrix of Knowledge Point
Question Quantity Associated with Knowledge Point in dataset_two
TOP-N 推荐模型 precision recall F
5 Pearson-CF 0.609 4 0.551 4 0.579 0
Edurank 0.630 6 0.578 5 0.603 4
CF-SPM 0.653 9 0.599 7 0.625 6
LS-PLRM 0.679 4 0.616 4 0.646 4
10 Pearson-CF 0.623 5 0.564 7 0.592 6
Edurank 0.657 5 0.583 5 0.618 3
CF-SPM 0.696 5 0.606 5 0.648 4
LS-PLRM 0.730 5 0.614 9 0.667 7
15 Pearson-CF 0.644 6 0.574 1 0.607 3
Edurank 0.682 1 0.593 1 0.634 5
CF-SPM 0.717 0 0.605 0 0.656 3
LS-PLRM 0.728 7 0.621 6 0.670 9
20 Pearson-CF 0.654 7 0.585 2 0.618 0
Edurank 0.716 6 0.600 8 0.653 6
CF-SPM 0.717 8 0.611 9 0.660 6
LS-PLRM 0.737 9 0.631 2 0.680 4
Indicator Values of Recommendation Models
MAE Values of Recommendation Models
组别 人数 推荐模型
A 38 Pearson-CF
B 39 Edurank
C 38 CF-SPM
D 38 LS-PLRM
Grouping Information and Recommendation Models
Average Scores in Two Tests
Group N Mean Std.Deviation Std.Error.Mean
A 38 66.51 11.31 1.83
B 38 67.56 11.06 1.79
C 38 68.49 8.96 1.45
D 38 69.84 8.48 1.38
Statistics of Sub-samples
Group t sig(2-tailed)
comparison: A-D -2.59 0.014
comparison: B-D -2.39 0.022
comparison: C-D -2.14 0.039
Test of Paired Sub-samples
[1] Tarus J K, Niu Z, Mustafa G. Knowledge-based Recommendation: A Review of Ontology-based Recommender Systems for E-learning[J]. Artificial Intelligence Review, 2018,50:21-48.
[2] Lu J . A Personalized E-Learning Material Recommender System [C]// Proceedings of the 2nd International Conference on Information Technology for Application (ICITA 2004), Sydney, Australia. 2004: 374-379.
[3] 杨晋吉, 胡波, 王欣明, 等. 一种知识图谱的排序学习个性化推荐算法[J]. 小型微型计算机系统, 2018,39(11):2419-2423.
[3] ( Yang Jinji, Hu Bo, Wang Xinming, et al. Personalized Recommendation Algorithm for Learning to Rank by Knowledge Graph[J]. Journal of Chinese Computer Systems, 2018,39(11):2419-2423.)
[4] Zhao Z D, Shang M S . User-based Collaborative-Filtering Recommendation Algorithms on Hadoop [C]// Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand. 2010: 478-481.
[5] 李容, 李明奇, 郭文强. 基于改进相似度的协同过滤算法研究[J]. 计算机科学, 2016,43(12):206-208, 240.
[5] ( Li Rong, Li Mingqi, Guo Wenqiang. Research on Collaborative Filtering Algorithm with Improved Similarity[J]. Computer Science, 2016,43(12):206-208, 240.)
[6] 韩建华, 姜强, 赵蔚, 等. 智能导学环境下个性化学习模型及应用效能评价[J]. 电化教育研究, 2016,37(7):66-73.
[6] ( Han Jianhua, Jiang Qiang, Zhao Wei, et al. A Model of Personalized Learning in Intelligent Tutoring Environment and Its Evaluation[J]. E-education Research, 2016,37(7):66-73.)
[7] Bobadilla J, Serradilla F, Hernando A. Collaborative Filtering Adapted to Recommender Systems of E-learning[J]. Knowledge-Based Systems, 2009,22(4):261-265.
[8] Dwivedi P, Bharadwaj K K. Effective Trust-aware E-learning Recommender System Based on Learning Styles and Knowledge Levels[J]. Journal of Educational Technology & Society, 2013,16(4):201-216.
[9] Bourkoukou O, EI Bachari E, EI Adnani M. A Recommender Model in E-learning Environment[J]. Arabian Journal for Science and Engineering, 2017,42:607-617.
[10] Segal A, Gal K, Shani G, et al. A Difficulty Ranking Approach to Personalization in E-learning[J]. International Journal of Human-Computer Studies, 2019,130:261-272.
[11] Zheng Q H, Qian Y N, Liu J . Yotta: A Knowledge Map Centric E-Learning System [C]// Proceedings of the 7th International Conference on e-Business Engineering (ICEBE), Shanghai, China. 2010: 42-49.
[12] Wang M H, Huang C F, Yang T Y. Acceptance of Knowledge Map Systems: An Empirical Examination of System Characteristics and Knowledge Map Systems Self-efficacy[J]. Asia Pacific Management Review, 2012,17(3):263-280.
[13] 李士平, 赵蔚, 刘红霞, 等. 基于知识地图的自我导向学习设计与实证研究[J]. 电化教育研究, 2016,37(5):74-81.
[13] ( Li Shiping, Zhao Wei, Liu Hongxia, et al. The Design and Empirical Research of Knowledge-Map Based Self-Directed Learning[J]. E-education Research, 2016,37(5):74-81.)
[14] 柯立秋. 基于知识地图的学习资源融合系统设计与实现[D]. 武汉: 华中师范大学, 2018.
[14] ( Ke Liqiu. Design and Implementation of Learning Resource Fusion System Based on Knowledge Map[D]. Wuhan: Central China Normal University, 2018.)
[15] 马宏伟, 张光卫, 李鹏. 协同过滤推荐算法综述[J]. 小型微型计算机系统, 2009,30(7):1282-1288.
[15] ( Ma Hongwei, Zhang Guangwei, Li Peng. Survey of Collaborative Filtering Algorithms[J]. Journal of Chinese Computer Systems, 2009,30(7):1282-1288.)
[16] Zins C. Knowledge Map of Information Science[J]. Journal of the American Society for Information Science and Technology, 2007,58(4):526-535.
[17] 叶伟巍. 知识学习与技术创新[J]. 高等工程教育研究, 2016(5):74-79.
[17] ( Ye Weiwei. Research on Knowledge Gaining and Technological Innovation[J]. Research in Higher Education of Engineering, 2016(5):74-79.)
[18] 李松林. 知识教学的突破: 从知识到知识的知识[J]. 教育科学研究, 2016(1):60-64.
[18] ( Li Songlin. The Breakthrough of Knowledge Teaching: From Knowledge to Knowledge[J]. Educational Science Research, 2016(1):60-64.)
[19] Borgatti S P, Everett M G. A Graph-theoretic Perspective on Centrality[J]. Social Networks, 2006,28(4):466-484.
[20] Dong W, Moses C, Li K . Efficient K-Nearest Neighbor Graph Construction for Generic Similarity Measures [C]// Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India. 2011.
[21] Klašnja-Milićević A, Vesin B, Ivanović M, et al. E-Learning Personalization Based on Hybrid Recommendation Strategy and Learning Style Identification[J]. Computers & Education, 2011,56(3):885-899.
[22] de Winter J C F. Using the Student’s T-test with Extremely Small Sample Sizes[J]. Practical Assessment, Research, and Evaluation, 2013,18: Article No. 10.
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