  This paper proposes a novel neural network, joint training capsule network
(JTCN), for the cold start recommendation task. We propose to mimic the
high-level user preference other than the raw interaction history based on the
side information for the fresh users. Specifically, an attentive capsule layer
is proposed to aggregate high-level user preference from the low-level
interaction history via a dynamic routing-by-agreement mechanism. Moreover,
JTCN jointly trains the loss for mimicking the user preference and the softmax
loss for the recommendation together in an end-to-end manner. Experiments on
two publicly available datasets demonstrate the effectiveness of the proposed
model. JTCN improves other state-of-the-art methods at least 7.07% for
CiteULike and 16.85% for Amazon in terms of Recall@100 in cold start
recommendation.
