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Integrating Reputation to Recommendation Techniques in an e-learning Environment

机译：在电子学习环境中将声誉整合到推荐技术中

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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，我们建议调查声誉机制在电子学习环境中的整合，以生成个性化推荐。拟议的架构名为e-RecRep，旨在允许在电子学习环境中推荐LO，在该环境中考虑推荐这些LO的用户的声誉。随着e-RecRep的采用，学生从系统和其他用户那里收到与所研究内容有关的学习对象的建议，从而鼓励学生补充自己的学习内容。初步的结果使我们得出结论，向一个在团队中享有良好声誉的人的建议可以使所推荐的信息更加相关，不仅可以提高信息的可信度，还可以提高信息的健壮性，多样性和惊奇性（偶然性）。本文的结构如下。首先将介绍主要概念和相关工作。然后介绍RecRep体系结构及其主要功能，并通过实际使用场景描述其行为。最后，讨论了涉及其使用和工作连续性的第一个实验的结果。

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