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Multi-context Recommendation in Technology Enhanced Learning

机译:技术中的多语境推荐增强了学习

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Recommender Systems (RSs) have been applied recently in Technology Enhanced Learning (TEL) to let recommending relevant learning resources to teachers or learners. In this paper, we propose a novel recommendation technique that combines a fuzzy collaborative filtering algorithm with content based one to make better recommendation, using learners' preferences and importance of knowledge to recommend items with different context corresponding to their different interests and tastes. Empirical evaluations show that the proposed technique is feasible and effective.
机译:推荐系统(RSS)最近应用于技术增强学习(电话),让您向教师或学习者推荐相关的学习资源。在本文中,我们提出了一种新的推荐技术,该技术将模糊协作过滤算法与内容的内容中的一个更好的建议,利用学习者的偏好和知识的重要性推荐与他们不同的兴趣和口味相对应的不同上下文的项目。实证评估表明,该技术是可行和有效的。

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