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Integrating Reputation to Recommendation Techniques in an e-learning Environment

机译：将声誉整合到电子学习环境中的推荐技巧

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摘要

In this paper, we propose to investigate the incorporation of reputation mechanism in an e-learning environment in order to generate personalized recommendations. The proposed architecture, named e-RecRep, aims to allow the recommendation of LOs in an e-learning environment, where the reputation of users who recommend these LOs is considered. With the adoption of e-RecRep, the student receives suggestions - from the system and from other users - of learning objects that relate to the studied content, encouraging the student to complement his learning. Preliminary results allow us to conclude that suggestions from a person who presents a good reputation to a group make the recommended information more relevant, improving not only the credibility of the information but also its robustness, diversity and surprise (serendipity). The article will be structured as follows. The main concepts and related work will be presented initially. The RecRep architecture and its main features are then presented as well as the description of their behavior through real usage scenarios. Finally the results of the first experiments involving its use and the possibilities of continuity of the work are discussed.
机译：在本文中，我们建议调查在电子学习环境中的声誉机制纳入，以便产生个性化建议。拟议的架构是名为电子报发的，旨在允许LOS在电子学习环境中的建议，其中考虑推荐这些洛杉矶的用户的声誉。通过通过电子报复，学生从系统和其他用户获取建议 - 与学习内容相关的学习对象，鼓励学生补充他的学习。初步结果允许我们得出结论，从群体呈现良好声誉的人的建议使建议的信息更加相关，不仅改善信息的可信度，还改善了其鲁棒性，多样性和惊喜（Serendipity）。这篇文章将按如下方式构建。主要概念和相关工作最初将呈现。然后，通过真正的使用场景展示Remente架构及其主要特征以及他们行为的描述。最后，讨论了涉及其使用的第一个实验的结果和工作连续性的可能性。

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