Recommender systems appeared in the early 90s to help users deal with cognitive overload brought by the internet. From there to now, such systems have assumed many other roles like help users to explore, improve decision making, or even entertain. The system needs to look to user characteristics to accomplish such new goals. These characteristics help understand what the user task is and how to adapt the recommendation to support such task. Related research has proposed recommender systems in education. These recommender systems help learners to find the educational resources most fit for their needs. In this paper, we present an integration model between recommender and adaptive hypermedia systems. It results in a new process for educational resource recommendation, using a new algorithm of adaptive recommendation. Through a prototype and an online experiment on the educational scenario, we proved that AwARE could improve the recommendation accuracy, interaction with the system, and user satisfaction. Besides the prototype description, the paper presents a protocol to evaluate the proposed approach by both the providers’ and consumers’ point of view.

推荐系统出现在90年代初，旨在帮助用户应对互联网带来的认知超负荷。从那时到现在，此类系统已经承担了许多其他角色，例如帮助用户探索，改善决策乃至娱乐。系统需要依靠用户特征来实现这样的新目标。这些特征有助于了解用户任务是什么以及如何调整推荐以支持此类任务。相关研究提出了教育推荐系统。这些推荐系统可帮助学习者找到最适合他们需求的教育资源。在本文中，我们提出了推荐器和自适应超媒体系统之间的集成模型。它使用一种新的自适应推荐算法，导致了一个新的教育资源推荐过程。通过针对教育场景的原型和在线实验，我们证明了AwARE可以提高推荐准确性，与系统的交互以及用户满意度。除了原型描述之外，本文还提供了一种协议，可以从提供者和消费者的角度评估所提出的方法。