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

Connectionist recommendation in the wild: on the utility and scrutability of neural networks for... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png User Modeling and User-Adapted Interaction Springer Journals

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

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References (122)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer Nature B.V.
Subject
Computer Science; User Interfaces and Human Computer Interaction; Multimedia Information Systems; Management of Computing and Information Systems
ISSN
0924-1868
eISSN
1573-1391
DOI
10.1007/s11257-019-09218-7
Publisher site
See Article on Publisher Site

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.

Journal

User Modeling and User-Adapted InteractionSpringer Journals

Published: Feb 4, 2019

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