Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance
This work addresses course selection challenges for university students, offering an incremental improvement in recommender systems through neural network adaptations.
The paper tackled personalized course recommendation by applying recurrent neural networks and skip-gram models to student enrollment sequences, resulting in a deployed university system with improved scrutability and balance between inferred and explicit user preferences.
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.