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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 wordsLearning 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

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:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1092     OR     http://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
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