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Context-Aware Systems for Sequential Item Recommendation
Quizlet is the most popular online learning tool in the United States, and is
used by over 2/3 of high school students, and 1/2 of college students. With
more than 95% of Quizlet users reporting improved grades as a result, the
platform has become the de-facto tool used in millions of classrooms. In this
paper, we explore the task of recommending suitable content for a student to
study, given their prior interests, as well as what their peers are studying.
We propose a novel approach, i.e. Neural Educational Recommendation Engine
(NERE), to recommend educational content by leveraging student behaviors rather
than ratings. We have found that this approach better captures social factors
that are more aligned with learning. NERE is based on a recurrent neural
network that includes collaborative and content-based approaches for
recommendation, and takes into account any particular student's speed, mastery,
and experience to recommend the appropriate task. We train NERE by jointly
learning the user embeddings and content embeddings, and attempt to predict the
content embedding for the final timestamp. We also develop a confidence
estimator for our neural network, which is a crucial requirement for
productionizing this model. We apply NERE to Quizlet's proprietary dataset, and
present our results. We achieved an R^2 score of 0.81 in the content embedding
space, and a recall score of 54% on our 100 nearest neighbors. This vastly
exceeds the recall@100 score of 12% that a standard matrix-factorization
approach provides. We conclude with a discussion on how NERE will be deployed,
and position our work as one of the first educational recommender systems for
the K-12 space
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