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A personalized recommendation system in E-Learning environment based on semantic analysis

机译：基于语义分析的电子学习环境中的个性化推荐系统

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摘要

The proposal of various electronic learning contents, e.g. remote education or virtual classrooms, has given a powerful impetus to the E-Learning techniques. However, there still remain several hard and complicated problems unsolved. Especially when compared with e-commerce and medicine, the problems of the recommenders in E-Learning system have not been fully figured out. In this paper, a novel personalized semantic recommendation system (PSRS) for E-Learning is designed. The proposed PSRS system employs the Video Structurized Description (VSD) technique to extract the initial keywords description of the learning contents, and then adopts the lexical parsing technique to refine the descriptive words with a standard format according to the initial keywords. Subsequently, the PSRS adopts rules auto-updating (RAU) to automatically add sequential items into ontology rules. Depending on the specific ontology knowledge with domain rules, semantic mapping and intelligent reasoning techniques are applied to generate certain semantic related recommending items for the active learners. Experimental results indicate that the proposed PSRS preforms better in accuracy than any other existing algorithms.
机译：各种电子学习内容的提议，例如，远程教育或虚拟教室，对电子学习技术给予了强大的推动力。但是，仍然存在一些艰难而复杂的问题未解决。特别是与电子商务和医学相比，电子学习系统中推荐人的问题尚未完全弄清楚。本文设计了一种用于电子学习的新型个性化语义推荐系统（PSR）。所提出的PSRS系统采用视频结构化描述（VSD）技术来提取学习内容的初始关键字描述，然后采用根据初始关键字以标准格式优化描述性单词的词汇解析技术。随后，PSRS采用规则自动更新（RAU）以自动将顺序项添加到本体规则中。根据具有域规则的特定本体知识，应用语义映射和智能推理技术来为活动学习者生成某些语义相关的推荐项目。实验结果表明，所提出的PSRS比任何其他现有算法更好地预成型。

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