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Research on Personalized Recommendation System in E-learning Based on Collaborative Filtering Technology

Author: LiuMin
Tutor: SunHuaZhi；ZengTao
School: Tianjin Normal University
Course: Educational Technology
Keywords: E-learning Personalized Recommendation Collaborative Filtering Sparsity
CLC: TP391.6
Type: Master's thesis
Year: 2010
Downloads: 235
Quote: 2
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Abstract


The development and application of E-learning to provide learners with an unprecedented wealth of resources and flexible way of learning can accept multiple learners access learning platform server running, making resource utilization is increased, at the same time without time space and geographical constraints, enable learners to achieve a truly autonomous learning. But the current situation is, most of the Web-based E-learning platform is still based on the website are stereotyped for each learner to see the content can not be based on the student's specific situation \At the same time, learners increasingly strong desire personalized service platform, therefore design and build a personalized learning platform E-learning is becoming a topic of concern to many researchers. Because of this, the paper recommended system into the E-learning platform, so as to achieve the purpose of personalized service. Collaborative Filtering technology is an effective way to achieve a personalized recommendation system. Collaborative Filtering technology is introduced into the E-learning personalized recommendation system, the main research topics include: (1) user interest modeling. Static user interest information to obtain explicit feedback, implicit feedback to obtain a dynamic user interest information. And then using the vector space model to represent user interest model as well as the system of learning resources, and contain the original information to adjust the addition of technology to update user interest model. (2) a sparse matrix based on the evaluation of the improvements of the nearest neighbor collaborative filtering algorithm and through experiments verify the effectiveness and superiority of the algorithm, the experimental results show that the user evaluation value of the effect of the case of the proposed algorithm can effectively avoid sparse matrix The phenomenon is magnified, the accuracy is superior to the traditional algorithm. (3) E-LPIRS personalized recommendation prototype system design and implementation. Introduced from the module structure design, development platform and development tools, database table design, system recommended interface the E-LPIRS prototype system developed and run. The proposed algorithm can effectively alleviate the data sparseness problem, improve the quality of the recommendation system recommended. Personalized recommendation of E-LPIRS prototype system articles designed not only to provide learners with personalized learning resource recommendation service, also applies to other application areas of research and design, have a higher reference and the reference value.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Teaching machine, learning machine
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