Cold-start Sequential Recommendation via Meta Learner

• URL: http://arxiv.org/abs/2012.05462v1
• Date: Thu, 10 Dec 2020 05:23:13 GMT
• Title: Cold-start Sequential Recommendation via Meta Learner
• Authors: Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu
• Abstract summary: We propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user.
• Score: 10.491428090228768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold-start items, which are prevalent in those scenarios. As there is generally no side information in the setting of sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available. Thus, we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. This task is non-trivial as it targets at an important problem in a novel and challenging context. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user. Besides, our framework can be painlessly integrated with neural network-based models. Extensive experiments conducted on three real-world datasets verify the superiority of Mecos, with the average improvement up to 99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.

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