The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users’ environment and support them in their decision making and wayfinding. A novel application of recurrent neural networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a usability study leading to the ultimate deployment of the system at a university.

中文翻译：

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野外联结主义推荐：关于神经网络在个性化课程指导中的实用性和可审查性

用户的聚合行为可以共同编码与他们交互的对象的深层语义信息。在本文中，我们展示了这些数据的合成可以阐明用户环境地形并支持他们进行决策和寻路的新方法。循环神经网络和跳跃语法模型的一种新应用，由于它们在建模语言中的应用而流行的方法，被用于学生大学注册序列，以创建课程的向量表示并绘制它们之间的遍历。我们展示了如何从这些神经网络中获得可审查性，以及如何将这些技术的组合视为内容标记的演变以及推荐者平衡从数据推断出的用户偏好与明确指定的用户偏好的手段。从模型的验证到 UI 的开发，我们讨论了由可用性研究的结果提供的额外必要功能，导致系统在大学中的最终部署。