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首页> 外文会议>International conference of the CLEF Association >CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education
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CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education

机译：CLEF 2017 NewsREEL概述：基于流的推荐任务，用于评估和教育

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摘要

News recommender systems provide users with access to news stories that they find interesting and relevant. As other online, stream-based recommender systems, they face particular challenges, including limited information on users' preferences and also rapidly fluctuating item collections. In addition, technical aspects, such as response time and scalability, must be considered. Both algorithmic and technical considerations shape working requirements for real-world recommender systems in businesses. NewsREEL represents a unique opportunity to evaluate recommendation algorithms and for students to experience realistic conditions and to enlarge their skill sets. The NewsREEL Challenge requires participants to conduct data-driven experiments in NewsREEL Replay as well as deploy their best models into NewsREEL Live's 'living lab'. This paper presents NewsREEL 2017 and also provides insights into the effectiveness of NewsREEL to support the goals of instructors teaching recommender systems to students. We discuss the experiences of NewsREEL participants as well as those of instructors teaching recommender systems to students, and in this way, we showcase NewsREEL's ability to support the education of future data scientists.
机译：新闻推荐器系统使用户可以访问他们认为有趣且相关的新闻故事。与其他基于流的在线推荐系统一样，它们也面临着特殊的挑战，包括关于用户偏好的有限信息以及项目集合的快速波动。另外，必须考虑技术方面，例如响应时间和可伸缩性。算法和技术方面的考虑都影响着企业中实际推荐系统的工作要求。 NewsREEL代表了一个独特的机会，可以评估推荐算法，并让学生体验现实条件并扩大他们的技能。 NewsREEL挑战赛要求参​​与者在NewsREEL Replay中进行数据驱动的实验，并将最佳模型部署到NewsREEL Live的“生活实验室”中。本文介绍了NewsREEL 2017，还提供了有关NewsREEL的有效性的见解，以支持讲师向学生教授推荐系统的目标。我们讨论了NewsREEL参与者的经验以及向学生教授推荐系统的讲师的经验，并以此方式展示了NewsREEL支持未来数据科学家的教育的能力。

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