学习资源精准推荐模型及应用研究
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摘要
根据属性特征推荐资源,由于存在冷启动和稀疏性问题,限制了在线学习资源推荐的性能.基于知识表示和协同过滤,将学习者的学习水平和学习风格等特征融入推荐过程,进行协同过滤个性化推荐,提出了一种学习资源精准推荐模型,构建了学习者和学习资源知识表示模型;通过实验表明知识表示-协同过滤相结合的推荐算法在个性化推荐和推荐准确度方面优于传统的CF算法.
Due to the existence of cold start and sparsity problems,the performance of online learning resource recommendation based on attribute characteristics is limited.This paper is based on knowledge representation and collaborative filtering.We integrate learners' learning levels and learning styles into the recommendation process and perform personalized recommendation based on collaborative filtering.Through the works of this paper,an accurate learning resource recommendation model is proposed,a knowledge representation model for learners and learning resources is built.Experiments show that the recommendation algorithm combined with knowledge representation and collaborative filtering outperforms the traditional CF algorithm in terms of personalized recommendation and recommendation accuracy.
Due to the existence of cold start and sparsity problems,the performance of online learning resource recommendation based on attribute characteristics is limited.This paper is based on knowledge representation and collaborative filtering.We integrate learners' learning levels and learning styles into the recommendation process and perform personalized recommendation based on collaborative filtering.Through the works of this paper,an accurate learning resource recommendation model is proposed,a knowledge representation model for learners and learning resources is built.Experiments show that the recommendation algorithm combined with knowledge representation and collaborative filtering outperforms the traditional CF algorithm in terms of personalized recommendation and recommendation accuracy.
引文
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[11]Tarus J,Niu Z,Khadidja B.E-Learning Recommender System Based on Collaborative Filtering and Ontology[J].World Academy of Science,Engineering and Technology,International Journal of Computer,Electrical,Automation,Control and Information Engineering,2017,11(2):225-230.
[12]王瑞,李永波,王晓东,等.移动自主学堂及其应用[J].河南师范大学学报(自然科学版),2014,42(6):162-166.
[13]Bobadilla J.Recommender systems survey[J].Knowledge-Based Systems,2013,46(1):109-132.
[2]叶海智,刘骏飞.基于用户评论的协同过滤推荐算法[J].河南师范大学学报(自然科学版),2017,45(1):79-84.
[3]Schafer J B,Dan F,Herlocker J,et al.Collaborative Filtering Recommender Systems[M].Berlin:Springer,2007:46-45.
[4]Najafabadi M K,Mahrin M N.A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback[J].Artificial Intelligence Review,2015,45(2):1-35.
[5]Le H S.HU-FCF++:A novel hybrid method for the new user cold-start problem in recommender systems[J].Engineering Applications of Artificial Intelligence,2015,41:207-222.
[6]Barjasteh I,Forsati R,Ross D,et al.Cold-Start Recommendation with Provable Guarantees:A Decoupled Approach[J].IEEE Transactions on Knowledge&Data Engineering,2016,28(6):1462-1474.
[7]Konstan J A,Walker J D,Brooks D C,et al.Teaching recommender systems at large scale:evaluation and lessons learned from a hybrid MOOC[J].ACM Transactions on Computer-Human Interaction(TOCHI),2015,22(2):10.
[8]王晓东.基于Ontology知识库系统建模与应用研究[D].上海:华东师范大学,2003.
[9]Ricci F.Introduction to Recommender Systems Handbook[M].Berlin:Springer,2011:1-4.
[10]贺桂和.基于用户偏好挖掘的电子商务协同过滤推荐算法研究[J].情报科学,2013(12):38-42.
[11]Tarus J,Niu Z,Khadidja B.E-Learning Recommender System Based on Collaborative Filtering and Ontology[J].World Academy of Science,Engineering and Technology,International Journal of Computer,Electrical,Automation,Control and Information Engineering,2017,11(2):225-230.
[12]王瑞,李永波,王晓东,等.移动自主学堂及其应用[J].河南师范大学学报(自然科学版),2014,42(6):162-166.
[13]Bobadilla J.Recommender systems survey[J].Knowledge-Based Systems,2013,46(1):109-132.