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.

本文提出了一种新颖的神经网络模型，即联合训练胶囊网络模型（JTCN），用于解决冷启动推荐任务。该模型通过引入侧信息模拟用户高级偏好，使用关注机制的胶囊层动态汇总用户高级偏好， 并将用户偏好的损失和推荐中的 softmax 损失联合训练。实验证明，该模型在 CiteULike 和 Amazon 数据集上都优于当前最先进的方法，其中冷启动推荐任务的召回率提高至少 7.07％（CiteULike）和 16.85％（Amazon）以上。