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Analyzing Learners’ Relationship to Improve the Quality of Recommender System for Group Learning Support

机译：分析学习者的关系，提高集团学习支持的推荐系统质量

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—Recommender systems are now a popular research area and have become powerful tools to present personalized offers to users in many domains (e.g. ecommerce, e-learning). In this paper, we introduced an approach of personalization which extracts learners’ relationship based on learning processes and learning activities (e.g. note taking) to provide more authenticity, personalized recommendations for group learning support. Base on learners’ learning activities some interaction factors are extracted by using natural language process technologies and data mining automatically. Then, extracted interaction factors are utilized to generate some relationship indicators for inferring the learners’ directive relationship. These indicators are as symbols in order to describe a situation and relative degree which knowledge and understanding are socially distributed among group learners. Thirdly, we use a machine learning approach for acquiring a learner relationship identify module according to the relationship indicators. The experimental result shows that the proposed approach can give a more satisfying and qualified recommendation.
机译：-Recommender系统现在是一个流行的研究区域，并成为在许多域中的用户提供个性化优惠的强大工具（例如电子商务，电子学习）。在本文中，我们介绍了一种个性化方法，这些方法基于学习过程和学习活动提取学习者的关系（例如，注意）为集团学习支持提供更多的真实性，个性化建议。基于学习者的学习活动，通过自动使用自然语言过程技术和数据挖掘来提取一些交互因素。然后，利用提取的相互作用因子来生成一些关系指标，以推断学习者的指示关系。这些指标作为符号，以描述集团学习者之间的社会分布的情况和相对程度的情况和相对程度。第三，我们使用机器学习方法来根据关系指示器获取学习者关系识别模块。实验结果表明，该方法可以提供更令人满意和合格的建议。

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