论文标题
冷启动推荐的联合培训胶囊网络
Joint Training Capsule Network for Cold Start Recommendation
论文作者
论文摘要
本文提出了一个新型的神经网络，即联合训练胶囊网络（JTCN），以进行冷启动推荐任务。我们建议根据新用户的侧面信息模仿除原始交互历史记录以外的高级用户偏好。具体而言，提出了一个细心的胶囊层，通过通过动态划分的机制来汇总低级交互历史记录的高级用户偏好。此外，JTCN共同训练以端到端方式一起模仿用户偏好的损失和推荐的软性损失。两个公开可用数据集的实验证明了该模型的有效性。 JTCN在Cold Start推荐中提高了Citeulike的其他最先进方法至少为7.07％，亚马逊提高了16.85％的召回@100。
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.
