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

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Abstract

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.

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Notes

  1. http://www.gao.gov/assets/690/686530.pdf.

  2. https://github.com/CAHLR/d3-scatterplot.

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Acknowledgements

We would like to thank the generous contributions by UC Berkeley administrators and staff; Andrew Eppig, Mark Chiang, Max Michel, Jen Stringer, and Walter Wong with a special thanks to associate registrar Johanna Metzgar for her partnership in the deployment of the system. We would like to also thank the following undergraduate student research assistants for their contributions to the system’s development; Christopher Vu Le, Andrew Joo Hun Nam, Arshad Ali Abdul Samad, Alessandra Silviera, Divyansh Agarwal, and Yuetian Luo. This work was supported by Grants from the National Science Foundation (Awards 1547055, 1446641).

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    Zachary A. Pardos, Zihao Fan & Weijie Jiang

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Pardos, Z.A., Fan, Z. & Jiang, W. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Model User-Adap Inter 29, 487–525 (2019). https://doi.org/10.1007/s11257-019-09218-7

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