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

Joint Training Capsule Network for Cold Start Recommendation