  Classical recommender system methods typically face the filter bubble problem
when users only receive recommendations of their familiar items, making them
bored and dissatisfied. To address the filter bubble problem, unexpected
recommendations have been proposed to recommend items significantly deviating
from user's prior expectations and thus surprising them by presenting "fresh"
and previously unexplored items to the users. In this paper, we describe a
novel Personalized Unexpected Recommender System (PURS) model that incorporates
unexpectedness into the recommendation process by providing multi-cluster
modeling of user interests in the latent space and personalized unexpectedness
via the self-attention mechanism and via selection of an appropriate unexpected
activation function. Extensive offline experiments on three real-world datasets
illustrate that the proposed PURS model significantly outperforms the
state-of-the-art baseline approaches in terms of both accuracy and
unexpectedness measures. In addition, we conduct an online A/B test at a major
video platform Alibaba-Youku, where our model achieves over 3\% increase in the
average video view per user metric. The proposed model is in the process of
being deployed by the company.
