  Unexpected recommender system constitutes an important tool to tackle the
problem of filter bubbles and user boredom, which aims at providing unexpected
and satisfying recommendations to target users at the same time. Previous
unexpected recommendation methods only focus on the straightforward relations
between current recommendations and user expectations by modeling
unexpectedness in the feature space, thus resulting in the loss of accuracy
measures in order to improve unexpectedness performance. Contrast to these
prior models, we propose to model unexpectedness in the latent space of user
and item embeddings, which allows to capture hidden and complex relations
between new recommendations and historic purchases. In addition, we develop a
novel Latent Closure (LC) method to construct hybrid utility function and
provide unexpected recommendations based on the proposed model. Extensive
experiments on three real-world datasets illustrate superiority of our proposed
approach over the state-of-the-art unexpected recommendation models, which
leads to significant increase in unexpectedness measure without sacrificing any
accuracy metric under all experimental settings in this paper.
