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Cold-start Sequential Recommendation via Meta Learner by Zheng, Yujia; Liu, Siyi; Li, Zekun; Wu, Shu is a scholarly article available to read on EtoBox.

What is Cold-start Sequential Recommendation via Meta Learner about?

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 improv

Author

Zheng, Yujia; Liu, Siyi; Li, Zekun; Wu, Shu

Published

2020

Language

EN