  Providing unexpected recommendations is an important task for recommender
systems. To do this, we need to start from the expectations of users and
deviate from these expectations when recommending items. Previously proposed
approaches model user expectations in the feature space, making them limited to
the items that the user has visited or expected by the deduction of associated
rules, without including the items that the user could also expect from the
latent, complex and heterogeneous interactions between users, items and
entities. In this paper, we define unexpectedness in the latent space rather
than in the feature space and develop a novel Latent Convex Hull (LCH) method
to provide unexpected recommendations. Extensive experiments on two real-world
datasets demonstrate the effectiveness of the proposed model that significantly
outperforms alternative state-of-the-art unexpected recommendation methods in
terms of unexpectedness measures while achieving the same level of accuracy.
