Cold-start Sequential Recommendation via Meta Learner
Keywords:Recommender Systems & Collaborative Filtering, Transfer/Adaptation/Multi-task/Meta/Automated Learning
AbstractThis 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 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.
How to Cite
Zheng, Y., Liu, S., Li, Z., & Wu, S. (2021). Cold-start Sequential Recommendation via Meta Learner. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4706-4713. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16601
AAAI Technical Track on Data Mining and Knowledge Management