Quaternion Collaborative Filtering for Recommendation

Quaternion Collaborative Filtering for Recommendation

Shuai Zhang, Lina Yao, Lucas Vinh Tran, Aston Zhang, Yi Tay

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 4313-4319. https://doi.org/10.24963/ijcai.2019/599

This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of Hamilton products. Quaternion representations, based on hypercomplex numbers, enable rich inter-latent dependencies between imaginary components. This encourages intricate relations to be captured when learning user-item interactions, serving as a strong inductive bias  as compared with the real-space inner product. All in all, we conduct extensive experiments on six real-world datasets, demonstrating the effectiveness of Quaternion algebra in recommender systems. The results exhibit that QCF outperforms a wide spectrum of strong neural baselines on all datasets. Ablative experiments confirm the effectiveness of Hamilton-based composition over multi-embedding composition in real space. 
Keywords:
Machine Learning: Deep Learning
Machine Learning: Recommender Systems