[Objective] This paper aims to improve traditionlal recommendation method and quality of E-Learning enviroment, which used attributes and access orders of resources in learning tree to predict learner’s rate. The collaborative filtering recommendation was then carried out through similar learner clustring. [Methods] First, “attributes of resources”“resource access order” “learning frequency and time” were standardized to construct users’ learning tree and then predict resouces rating. Second, learner’s similarity was calculated through Pearson and Cosine function respectivly based on predicted ratings. Third, K-means clustering method was used to group similar learners to establish collaborative filteing system for online E-learing. [Results] Compared with traditional collaborative filtering method, F-measure experimental result of the proposed method was 8.22% higher than the singular value decomposition CF and was 3.75% higher than the average score forecast CF. [Limitations] The proposed method was only tested on the dataset from one online learing platform with 52,456 students’ learning records and access logs. More research is needed to examine the method with other data sets. [Conclusions] The proposed collaborative filtering recommendation system does not rely on learners’ ratings and considers the influence of learners’ interest changes. It could help us deal with the starting and expanding issues.
马莉. 一种利用用户学习树改进的协同过滤推荐方法[J]. 现代图书情报技术, 2016, 32(4): 72-80.
Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree. New Technology of Library and Information Service, 2016, 32(4): 72-80.
Verbert K, Manouselis N, Ochoa X, et al.Context-Aware Recommender Systems for Learning: A Survey and Future Challenges[J]. IEEE Transactions on Learning Technologies, 2012, 5(4): 318-335.
[2]
Khribi M K, Jemni M, Nasraoui O.Automatic Recommendations for E-learning Personalization Based on Web Usage Mining Techniques and Information Retrieval[J]. Educational Technology and Society, 2009, 12(4): 30-42.
[3]
Sharif N, Afzal M T, Helic D.A Framework for Resource Recommendations for Learners Using Social Bookmarking [C]. In: Proceedings of the 8th International Conference on Computing and Networking Technology. IEEE, 2012: 71-76.
[4]
Salehi M, Kamalabadi I N, Ghoushchi M B G. Personalized Recommendation of Learning Material Using Sequential Pattern Mining and Attribute Based Collaborative Filtering[J]. Education and Information Technologies, 2014, 19(4): 713-735.
[5]
Salehi M, Kamalabadi I N.Hybrid Recommendation Approach for Learning Material Based on Sequential Pattern of the Accessed Material and the Learner’s Preference Tree[J]. Knowledge-Based Systems, 2013, 48: 57-69.
Aher S B, Lobo L.Applicability of Data Mining Algorithms for Recommendation System in E-learning [C]. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics. 2012: 1034-1040.
[8]
Salehi M, Kamalabadi I N, Attribute-based Recommender System for Learning Resource by Learner Preference Tree[C]. In: Proceedings of the 2nd International e-Conference on Computer and Knowledge Engineering. IEEE, 2012: 133-138.
[9]
Ge L, Kong W, Luo J.Courseware Recommendation in E-learning System [C]. In: Proceedings of the 5th International Conference on Advances in Web Based Learning. 2006: 10-24.
[10]
Wan L, Zhao C.A Hybrid Learning Object Recommendation Algorithm in E-learning Context[J]. International Journal of Digital Content Technology and Its Applications, 2012, 6(18): 442-448.
[11]
Wang S, Xie Y, Fang M.A Collaborative Filtering Recommendation Algorithm Based on Item and Cloud Model[J]. Wuhan University Journal of Natural Sciences, 2011, 16(1): 16-20.
[12]
Kim K, Ahn H.A Recommender System Using GA K-means Clustering in an Online Shopping Market[J]. Expert Systems with Applications, 2008, 34(2): 1200-1209.
[13]
Jalali M, Mustapha N, Sulaiman M N B, et al. OPWUMP: An Architecture for Online Predicting in WUM-Based Personalization System [A]. // Advances in Computer Science and Engineering[M]. Springer Berlin Heidelberg, 2009.
[14]
Albadvi A, Shahbazi M.Integrating Rating-based Collaborative Filtering with Customer Lifetime Value: New Product Recommendation Technique[J]. Intelligent Data Analysis, 2010, 14(1): 143-155.
[15]
Nielsen J. The 90-9-1 Rule for Participation Inequality in Social Media and Online Communities [EB/OL]. [2015-09-01]. .
[16]
Ebbinghaus H.Memory: A Contribution to Experimental Psychology[M]. New York: Dover, 1885.
[17]
Devi M K K, Venkatesh P. Kernel Based Collaborative Recommender System for E-Purchasing[J]. Academy of Sciences, 2010, 35(5): 513-524.
[18]
Kla?nja-Mili?evi? A, Vesin B, Ivanovic M, et al.E-learning Personalization Based on Hybrid Recommendation Strategy and Learning Style Identification[J]. Computers in Education, 2011, 56(3): 885-899.
[19]
Sarwar B M, Karypis G, Konstan J A, et al.Application of Dimensionality Reduction in Recommender Systems [EB/ OL]. [2015-10-25]. .
