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Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance

机译：野外的连接主义推荐：关于神经网络对个性化课程指导的实用性和特殊性

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摘要

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.
机译：用户的总行为可以集体编码有关它们交互的对象的深度语义信息。在本文中，我们展示了这些数据的合成的新方法可以照亮用户环境的地形，并在决策和路径采样中支持它们。一种新的经常性神经网络和Skip-Gram模型的应用，其应用于建模语言的应用方法，致力于学生大学注册序列，以创建课程的向量表示和映射到它们的遍历。我们提出了可以获得这些神经网络的支持性的示范，以及如何将这些技术的组合视为内容标记的演变和用于推荐用户的手段，以平衡从数据中推断的用户偏好与明确指定的那些。从验证模型到一个UI的开发中，我们讨论了额外的必要功能，通过了可用性研究的结果，导致系统在大学的最终部署。

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