Cold-start Sequential Recommendation via Meta Learner

TL;DR

This paper introduces Mecos, a meta-learning framework that significantly improves cold-start item recommendations in sequential recommendation systems by effectively utilizing limited user interactions.

Contribution

It proposes a novel meta-learning-based approach for cold-start sequential recommendation that can be integrated with neural models and outperforms existing methods.

Findings

Achieves up to 99% improvement in HR@10 on real datasets.

Effectively extracts user preferences from limited interactions.

Outperforms state-of-the-art baselines significantly.

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.