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A Collaborative Recommender System Based on Space-Time Similarities

机译：基于时空相似度的协同推荐系统

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摘要

The Internet of Things (IoT) concept promises a world of networked and interconnected devices that provides relevant content to users. Recommender systems can find relevant content for users in IoT environments, offering a user-adapted personalized experience. Collaboration-based recommenders in IoT environments rely on user-to-object, space-time interaction patterns. This extension of that idea takes into account user location and interaction time to recommend scattered, pervasive context-embedded networked objects. The authors compare their proposed system to memory-based collaborative methods in which user similarity is based on the ratings of previously rated items. Their proof-of-concept implementation was used in a real-world scenario involving 15 students interacting with 75 objects at Carlos III University of Madrid.
机译：物联网（IoT）概念承诺了一个联网和互连的设备世界，可为用户提供相关内容。推荐系统可以在IoT环境中为用户找到相关内容，从而提供用户自适应的个性化体验。物联网环境中基于协作的推荐器依赖于用户到对象的时空交互模式。这种想法的扩展考虑了用户位置和交互时间，以推荐分散的，无处不在的上下文嵌入式网络对象。作者将他们提出的系统与基于内存的协作方法进行了比较，在这种方法中，用户相似性是基于先前评分项目的评分。他们的概念验证实现已在马德里卡洛斯三世大学的15名学生与75个对象进行交互的实际场景中使用。

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