Research on Personalized Courseware Recommendation System of Rural Distance Learning Based on Combination Recommendation Technology

Abstract:

This paper proposed a model of combination recommendation, focusing on analysis and comparison of content filtering recommendation technology and collaborative filtering recommendation technology based on the mainstream personalized recommendation technology, and the model working process is given. For how to solve the problem of sparse and cold start, the paper gave the solve methods, and discussed the process of combination recommendation algorithm, and then introduced a method of developing and designing personalized courseware recommendation system of rural modern distance learning, and introduced the optimization measures of functional modules and performance. It provides a useful reference for distance education site to carry out personalized training services for rural adult users.

• Periodical: Applied Mechanics and Materials (Volumes 373-375)
• Pages: 1652-1660
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.373-375.1652
• Online since: August 2013
• Authors: Jian Xin Guo, Ji Chun Zhao
• Keywords: Combination Recommendation, Distance Learning, Interest Model, Personalized Learning

References

[1] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions[J]. IEEE Trans. on Knowledge and DataEngineering, 2005, 17(6): 734-749.

DOI: 10.1109/tkde.2005.99

[2] Yijin Shi, Ruiying Ma, Zhiyang Jia. Research on Recommendation Method of E － learning Systems, Iinformation Technology and Informatization [J]. 2012, 2: 51-54. (in chinese).

[3] Yuzhen Shi, Hao Zheng. Research on personalized recommendation system based on collaborative filtering techniques. Electronic Design Engineering [J]. 2012, 20(6): 41-44. (in Chinese).

[4] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J]. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749.

DOI: 10.1109/tkde.2005.99

[5] Chun Zeng, Chunxiao Xing, Lizhu Zhou. Review of personalized serve technology [J]. Journal of Software, 2002, 13(10): 1952-1960. (in Chinese).

[6] Qing Yang, Junli Sun, Jinqiao Wang, Zhiyong Jin. Semantic Web- Based Personalized Recommendation System of Courses Knowledge Research. 2010 International Conference on Intelligent Computing and Cognitive Informatics, 2010, 214-217.

DOI: 10.1109/icicci.2010.54

[7] Sun Liying. Research on Learning materials Personalized Recommendation based on combination recommended technology. A master thesis of Dalian Maritime University, 2010, 6. (in Chinese).

[8] Chi Zhang, Gang Chen, Huimin Wang. Recommendation Model based on blending recommendation technology. Computer Engineering, 2010, 36(22): 248-253. (in Chinese).

[9] Dan Liu, Peipei Chu, Lijuan Zheng. Research on and Practice the Collaborative Filtering Algorithms for Personalized Recommendation [J]。Journal of Shijiazhuang Institute of Railway Technology, 2008, 7(2): 43-48. (in Chinese).