  Collaborative filtering based algorithms, including Recurrent Neural Networks
(RNN), tend towards predicting a perpetuation of past observed behavior. In a
recommendation context, this can lead to an overly narrow set of suggestions
lacking in serendipity and inadvertently placing the user in what is known as a
"filter bubble." In this paper, we grapple with the issue of the filter bubble
in the context of a course recommendation system in production at a public
university. Most universities in the United States encourage students to
explore developing interests while simultaneously advising them to adhere to
course taking norms which progress them towards graduation. These competing
objectives, and the stakes involved for students, make this context a
particularly meaningful one for investigating real-world recommendation
strategies. We introduce a novel modification to the skip-gram model applied to
nine years of historic course enrollment sequences to learn course vector
representations used to diversify recommendations based on similarity to a
student's specified favorite course. This model, which we call multifactor2vec,
is intended to improve the semantics of the primary token embedding by also
learning embeddings of potentially conflated factors of the token (e.g.,
instructor). Our offline testing found this model improved accuracy and recall
on our course similarity and analogy validation sets over a standard skip-gram.
Incorporating course catalog description text resulted in further improvements.
We compare the performance of these models to the system's existing RNN-based
recommendations with a user study of undergraduates (N = 70) rating six
characteristics of their course recommendations. Results of the user study show
a dramatic lack of novelty in RNN recommendations and depict the characteristic
trade-offs that make serendipity difficult to achieve.
