Joint Training Capsule Network for Cold Start Recommendation

• URL: http://arxiv.org/abs/2005.11467v1
• Date: Sat, 23 May 2020 04:27:38 GMT
• Title: Joint Training Capsule Network for Cold Start Recommendation
• Authors: Tingting Liang, Congying Xia, Yuyu Yin, Philip S. Yu
• Abstract summary: This paper proposes a novel neural network, joint training capsule network (JTCN) for the cold start recommendation task. An attentive capsule layer is proposed to aggregate high-level user preference from the low-level interaction history. Experiments on two publicly available datasets demonstrate the effectiveness of the proposed model.
• Score: 64.35879555545749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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