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A Novel Multi-agent Community Building Scheme Based on Collaboration Filtering

机译：基于协作滤波的新型多功能社区建设方案

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Research on e-learner community building has attracted much attention for its effectiveness in sharing the learning experience and resources among geographically dispersed e-learners. While collaborative filtering proves its success as one of the most efficient methods in finding similar users in e-commerce domain, it does meet special challenges in e-learning areas. In this paper, we incorporate multi-agent techniques into collaborative filtering and propose a novel community building scheme. By doing so, we manage to collect useful information from the learner behaviors and thus increase the scalability and flexibility of traditional collaborative filtering methods. The experiment on a standard benchmark shows that our scheme has reasonable community building quality and e-learners can make better recommendations to each other inside the community.
机译：电子学习者社区建设的研究吸引了巨大关注其在地理位置分散的电子学习者之间分享学习经验和资源的有效性。虽然协作过滤证明其成功作为在电子商务领域找到类似用户的最有效的方法之一，但它确实符合电子学习领域的特殊挑战。在本文中，我们将多种子体技术纳入协作过滤并提出了一种新的社区建设方案。通过这样做，我们设法从学习者行为中收集有用的信息，从而提高传统协同过滤方法的可扩展性和灵活性。标准基准的实验表明，我们的计划具有合理的社区建设质量，电子学习者可以在社区内部互相提高建议。

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